Setup¶
Install in R:
install.packages("BiocManager")
BiocManager::install(c("edgeR", "BiocParallel", "RhpcBLASctl"))
Install
pip install toytree ete4 pybiomart
Options & Data¶
In [ ]:
%load_ext autoreload
%autoreload 2
import os
import re
import pickle
import matplotlib.pyplot as plt
import warnings
import logging
import seaborn as sns
from scipy.sparse import issparse
from pymer4.models import lmer, lm, glmer, compare
from pymer4 import config
import polars
import statsmodels.api as sm
import senepy
import gseapy as gp
import pertpy as pt
import scanpy as sc
import pandas as pd
import numpy as np
import scflow
from scflow.ax import find_senescence_genes, run_senepy
# Display/Environment Settings
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
warnings.filterwarnings("ignore", message=".*ast.Ellipsis is deprecated.*")
config.PANDAS_BACKEND = True
warnings.filterwarnings("ignore", message=".*vert.*will be deprecated.*")
pd.set_option("display.max_rows", 500) # or None for unlimited rows
pd.set_option("display.max_columns", 100)
pd.set_option("display.max_colwidth", 100)
pd.set_option("display.width", 300)
warnings.filterwarnings(
"ignore", message="Bitwise inversion '~' on bool is deprecated",
category=DeprecationWarning)
# Process Options
ncpus = os.cpu_count() - 1
overwrite = True
# If You Want Results Emailed (Will Overwrite HTML Regardless of `overwite`)
cur_file = os.path.join(os.path.abspath(""), "analyze_senescence_celltype"
"_specific_threshold.ipynb")
html_out = os.path.splitext(cur_file)[0] + ".html"
email = "elizabeth.aslinger@aya.yale.edu"
# set email to None to skip
# Cell Type Label Column
col_celltype = "annotation_scanvi"
# col_celltype = "annotation_by_overlap"
# col_celltype = "Annotation_ToppGene"
# col_celltype = "cellmap_class_name"
# col_celltype = "cellmap_class_name_collapsed"
# use_hierarchy = False
collapse_neurons = True # collapse neuron categories?
# Metadata Information
species = "Mouse"
col_sample, col_batch = "sample", "Group"
# col_age = "Characteristics[Age at Euthanasia]"
# col_condition = "Factor Value[Spaceflight]"
col_age = "Age_End"
col_condition = "Condition"
# group_order = ["Ground Control | 12 Weeks", "Ground Control | 29 Weeks",
# "Space Flight | 12 Weeks", "Space Flight | 29 Weeks"]
group_order = ["Ground Control | 20 Weeks", "Ground Control | 37 Weeks",
"Space Flight | 20 Weeks", "Space Flight | 37 Weeks"]
keys = {col_condition: dict(key_control="Ground Control",
key_treatment="Space Flight"),
col_age: dict(key_control="20 Weeks",
key_treatment="37 Weeks"),
# col_age: dict(key_control="12 Weeks",
# key_treatment="29 Weeks"),
col_batch: dict(key_control=group_order[0],
key_treatment=group_order[1:])}
palette = {col_condition: {keys[col_condition]["key_control"]: "b",
keys[col_condition]["key_treatment"]: "r"},
col_age: {keys[col_age]["key_control"]: "g",
keys[col_age]["key_treatment"]: "y"},
col_batch: dict(zip(group_order, ["c", "b", "tab:pink", "r"]))}
# Scoring Metrics
sen_metrics = ["senmayo"]
# sen_metrics = ["senepy", "senmayo"]
use_metric = "senmayo"
percentile = 1
# Regress Out Confounders?
vars_regress_out = ["Time"]
# File Input & Output
file_path = "data/OSD-613_integrated.h5ad"
file_path_new = os.path.splitext(file_path)[0] + "_analyzed.h5ad"
# Load Data
self = scflow.Rna(file_path, col_sample=col_sample, col_batch=col_batch,
col_celltype=col_celltype) # data
self.rna.obs.loc[:, col_batch] = self.rna.obs[col_batch].astype(
pd.CategoricalDtype(categories=group_order, ordered=True))
self.rna = self.rna[:, self.rna.var.mt == False] # snRNA-seq so remove MT-
self.rna = self.rna[:, self.rna.var.ribo == False] # snRNA-seq so remove RB-
self.rna.obs.loc[:, "Spaceflight"] = (self.rna.obs[col_condition] == keys[
col_condition]["key_treatment"]).astype(int) # 1 vs. 0 spaceflight
self.rna.obs.loc[:, "Aged"] = (self.rna.obs[col_age] == keys[
col_age]["key_treatment"]).astype(int) # 1 vs. 0 older
self.rna.X = self.rna.layers["log1p"].copy()
self.rna.raw = None
# Hierarchical
keys_cts = {"Neurons": ["Excitatory", "Inhibitory",
"Excitatory-Inhibitory", "Neuron"],
"Macroglia": ["Oligodendrocyte", "OPC", "Astrocyte"],
"Microglia": ["Microglial"],
"Endothelial Cell": ["Endothelial"]}
key_cts = dict(pd.concat([pd.Series(dict(([(i, x) for i in keys_cts[
x]]))) for x in keys_cts]))
if collapse_neurons is True:
self.rna.obs.loc[:, f"{col_celltype}_collapsed"] = self.rna.obs[
col_celltype].replace(dict(zip(keys_cts["Neurons"], ["Neurons"]*3)))
col_celltype = f"{col_celltype}_collapsed"
self.col_celltype = col_celltype
self.rna.obs.loc[:, f"{col_celltype}_hierarchy"] = self.rna.obs[
col_celltype].replace(key_cts)
# Display Object
print(self.rna)
print(self.rna.obs.groupby(col_sample).describe())
self.rna.obs
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/senepy/load_hubs.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. from pkg_resources import resource_filename /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/__init__.py:17: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. __version_info__ = tuple(LooseVersion(__version__).version) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/fields.py:198: RemovedInMarshmallow4Warning: Passing field metadata as a keyword arg is deprecated. Use the explicit `metadata=...` argument instead. warnings.warn(
AnnData object with n_obs × n_vars = 103274 × 12097
obs: 'Group', 'sample', 'Sample Number', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'Time', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual', 'annotation_scanvi', 'kws_integrate', 'leiden', 'leiden_resolution', 'leiden_n_neighbors', 'leiden_min_dist', 'leiden_subcluster', 'resolution_leiden_subcluster', 'annotation_by_overlap', 'majority_voting_short', 'Age_Start', 'Age_End', 'Condition', 'annotation_toppgene', 'predicted_labels', 'majority_voting', 'majority_voting_probabilities', 'annotation_majority_voting', 'Spaceflight', 'Aged', 'annotation_scanvi_collapsed', 'annotation_scanvi_collapsed_hierarchy'
var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
uns: 'Group_colors', 'annotation_majority_voting_colors', 'annotation_scanvi_colors', 'hvg', 'leiden', 'leiden_colors', 'leiden_subcluster', 'leiden_subcluster_colors', 'log1p', 'neighbors', 'over_clustering', 'rank_genes_groups_leiden', 'rank_genes_groups_leiden_subcluster', 'sample_colors', 'umap'
obsm: 'X_pca', 'X_pca_old', 'X_scANVI', 'X_umap'
layers: 'counts', 'log1p', 'scaled'
obsp: 'connectivities', 'distances'
Factor Value[Age] Characteristics[Age at Euthanasia] Parameter Value[duration] Parameter Value[Sample Storage Temperature] \
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25%
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 7547.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 7547.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 7547.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 5465.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 5465.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5465.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 5842.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 5842.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5842.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 4922.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 4922.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 4922.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 7539.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 7539.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 7539.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 3595.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 3595.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3595.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 5095.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 5095.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5095.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3669.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3669.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3669.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 5747.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 5747.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5747.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 5265.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 5265.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5265.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 3864.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 3864.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3864.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3672.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3672.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3672.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3730.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3730.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3730.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 1321.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 1321.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 1321.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 18386.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 18386.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 18386.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 18386.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 4050.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 4050.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 4050.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 9651.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 9651.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 9651.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 9651.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3914.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3914.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3914.0 -80.0 0.0 -80.0 -80.0
Time n_cells_original_sample n_genes_by_counts ... leiden_resolution leiden_n_neighbors \
50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean ... 75% max count mean std min 25%
sample ...
RRRM2_BRN_GC_ISS-T_YNG_GY4 -80.0 -80.0 -80.0 7547.0 1149.0 0.0 1149.0 1149.0 1149.0 1149.0 1149.0 7547.0 8542.0 0.0 8542.0 8542.0 8542.0 8542.0 8542.0 7547.0 1464.316152 ... 0.0075 0.0075 7547.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 -80.0 -80.0 -80.0 5465.0 961.0 0.0 961.0 961.0 961.0 961.0 961.0 5465.0 6044.0 0.0 6044.0 6044.0 6044.0 6044.0 6044.0 5465.0 2703.472827 ... 0.0075 0.0075 5465.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 -80.0 -80.0 -80.0 5842.0 1064.0 0.0 1064.0 1064.0 1064.0 1064.0 1064.0 5842.0 6503.0 0.0 6503.0 6503.0 6503.0 6503.0 6503.0 5842.0 1678.743923 ... 0.0075 0.0075 5842.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 -80.0 -80.0 -80.0 4922.0 915.0 0.0 915.0 915.0 915.0 915.0 915.0 4922.0 5641.0 0.0 5641.0 5641.0 5641.0 5641.0 5641.0 4922.0 2467.499187 ... 0.0075 0.0075 4922.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 -80.0 -80.0 -80.0 7539.0 1085.0 0.0 1085.0 1085.0 1085.0 1085.0 1085.0 7539.0 8476.0 0.0 8476.0 8476.0 8476.0 8476.0 8476.0 7539.0 2632.348985 ... 0.0075 0.0075 7539.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 -80.0 -80.0 -80.0 3595.0 1139.0 0.0 1139.0 1139.0 1139.0 1139.0 1139.0 3595.0 3880.0 0.0 3880.0 3880.0 3880.0 3880.0 3880.0 3595.0 2588.294298 ... 0.0075 0.0075 3595.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 -80.0 -80.0 -80.0 5095.0 707.0 0.0 707.0 707.0 707.0 707.0 707.0 5095.0 5658.0 0.0 5658.0 5658.0 5658.0 5658.0 5658.0 5095.0 2789.168008 ... 0.0075 0.0075 5095.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 -80.0 -80.0 -80.0 3669.0 661.0 0.0 661.0 661.0 661.0 661.0 661.0 3669.0 4238.0 0.0 4238.0 4238.0 4238.0 4238.0 4238.0 3669.0 1567.765604 ... 0.0075 0.0075 3669.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 -80.0 -80.0 -80.0 5747.0 869.0 0.0 869.0 869.0 869.0 869.0 869.0 5747.0 7265.0 0.0 7265.0 7265.0 7265.0 7265.0 7265.0 5747.0 1814.084218 ... 0.0075 0.0075 5747.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 -80.0 -80.0 -80.0 5265.0 1210.0 0.0 1210.0 1210.0 1210.0 1210.0 1210.0 5265.0 5776.0 0.0 5776.0 5776.0 5776.0 5776.0 5776.0 5265.0 2760.520608 ... 0.0075 0.0075 5265.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 -80.0 -80.0 -80.0 3864.0 870.0 0.0 870.0 870.0 870.0 870.0 870.0 3864.0 4260.0 0.0 4260.0 4260.0 4260.0 4260.0 4260.0 3864.0 2855.340321 ... 0.0075 0.0075 3864.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 -80.0 -80.0 -80.0 3672.0 1082.0 0.0 1082.0 1082.0 1082.0 1082.0 1082.0 3672.0 3968.0 0.0 3968.0 3968.0 3968.0 3968.0 3968.0 3672.0 3256.512255 ... 0.0075 0.0075 3672.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 -80.0 -80.0 -80.0 3730.0 660.0 0.0 660.0 660.0 660.0 660.0 660.0 3730.0 4125.0 0.0 4125.0 4125.0 4125.0 4125.0 4125.0 3730.0 3048.478284 ... 0.0075 0.0075 3730.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 -80.0 -80.0 -80.0 1321.0 704.0 0.0 704.0 704.0 704.0 704.0 704.0 1321.0 1493.0 0.0 1493.0 1493.0 1493.0 1493.0 1493.0 1321.0 1323.376230 ... 0.0075 0.0075 1321.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 -80.0 -80.0 -80.0 18386.0 1019.0 0.0 1019.0 1019.0 1019.0 1019.0 1019.0 18386.0 20000.0 0.0 20000.0 20000.0 20000.0 20000.0 20000.0 18386.0 996.987110 ... 0.0075 0.0075 18386.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 -80.0 -80.0 -80.0 4050.0 650.0 0.0 650.0 650.0 650.0 650.0 650.0 4050.0 4863.0 0.0 4863.0 4863.0 4863.0 4863.0 4863.0 4050.0 3231.898025 ... 0.0075 0.0075 4050.0 15.0 0.0 15.0 15.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 -80.0 -80.0 -80.0 9651.0 1107.0 0.0 1107.0 1107.0 1107.0 1107.0 1107.0 9651.0 10779.0 0.0 10779.0 10779.0 10779.0 10779.0 10779.0 9651.0 2324.572998 ... 0.0075 0.0075 9651.0 15.0 0.0 15.0 15.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 -80.0 -80.0 -80.0 3914.0 885.0 0.0 885.0 885.0 885.0 885.0 885.0 3914.0 4714.0 0.0 4714.0 4714.0 4714.0 4714.0 4714.0 3914.0 3249.220235 ... 0.0075 0.0075 3914.0 15.0 0.0 15.0 15.0
leiden_min_dist resolution_leiden_subcluster majority_voting_probabilities \
50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 15.0 15.0 15.0 7547.0 1.8 4.441186e-16 1.8 1.8 1.8 1.8 1.8 7547.0 0.005 8.674192e-19 0.005 0.005 0.005 0.005 0.005 5985.0 0.728018 0.354468 3.282433e-08 0.500470 0.935991 0.994391 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 15.0 15.0 15.0 5465.0 1.8 6.661948e-16 1.8 1.8 1.8 1.8 1.8 5465.0 0.005 8.674411e-19 0.005 0.005 0.005 0.005 0.005 5020.0 0.812074 0.320982 1.326950e-08 0.820617 0.984462 0.998907 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 15.0 15.0 15.0 5842.0 1.8 0.000000e+00 1.8 1.8 1.8 1.8 1.8 5842.0 0.005 8.674360e-19 0.005 0.005 0.005 0.005 0.005 5406.0 0.764825 0.332948 4.650237e-10 0.621054 0.956966 0.995094 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 15.0 15.0 15.0 4922.0 1.8 4.441343e-16 1.8 1.8 1.8 1.8 1.8 4922.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 4787.0 0.808646 0.316059 3.679113e-12 0.772958 0.982582 0.998239 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 15.0 15.0 15.0 7539.0 1.8 4.441187e-16 1.8 1.8 1.8 1.8 1.8 7539.0 0.005 8.674193e-19 0.005 0.005 0.005 0.005 0.005 7081.0 0.823671 0.311928 9.216436e-11 0.848215 0.986549 0.998814 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 15.0 15.0 15.0 3595.0 1.8 4.441510e-16 1.8 1.8 1.8 1.8 1.8 3595.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3306.0 0.848197 0.302598 4.892193e-13 0.911656 0.996120 0.999868 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 15.0 15.0 15.0 5095.0 1.8 2.220664e-16 1.8 1.8 1.8 1.8 1.8 5095.0 0.005 8.674469e-19 0.005 0.005 0.005 0.005 0.005 4562.0 0.798287 0.335168 2.489281e-06 0.771845 0.987226 0.999250 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 15.0 15.0 15.0 3669.0 1.8 4.441497e-16 1.8 1.8 1.8 1.8 1.8 3669.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3377.0 0.718330 0.355182 2.563253e-05 0.474151 0.925275 0.993546 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 15.0 15.0 15.0 5747.0 1.8 4.441279e-16 1.8 1.8 1.8 1.8 1.8 5747.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3944.0 0.815628 0.314699 7.947201e-11 0.819162 0.982113 0.998139 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 15.0 15.0 15.0 5265.0 1.8 0.000000e+00 1.8 1.8 1.8 1.8 1.8 5265.0 0.005 8.674441e-19 0.005 0.005 0.005 0.005 0.005 4800.0 0.845268 0.298211 1.508838e-09 0.898036 0.992874 0.999547 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 15.0 15.0 15.0 3864.0 1.8 6.662200e-16 1.8 1.8 1.8 1.8 1.8 3864.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3611.0 0.842997 0.299145 4.102560e-09 0.892723 0.991445 0.999444 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 15.0 15.0 15.0 3672.0 1.8 4.441497e-16 1.8 1.8 1.8 1.8 1.8 3672.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3293.0 0.835006 0.300631 9.044592e-08 0.863981 0.990512 0.999500 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 15.0 15.0 15.0 3730.0 1.8 4.441488e-16 1.8 1.8 1.8 1.8 1.8 3730.0 0.005 8.674780e-19 0.005 0.005 0.005 0.005 0.005 3458.0 0.820929 0.321241 9.661173e-08 0.844128 0.989742 0.999332 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 15.0 15.0 15.0 1321.0 1.8 0.000000e+00 1.8 1.8 1.8 1.8 1.8 1321.0 0.005 8.676902e-19 0.005 0.005 0.005 0.005 0.005 615.0 0.771479 0.333113 1.526836e-05 0.681785 0.965615 0.996596 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 15.0 15.0 15.0 18386.0 1.8 4.441013e-16 1.8 1.8 1.8 1.8 1.8 18386.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 5992.0 0.722214 0.355449 2.281540e-10 0.464056 0.934043 0.993215 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 15.0 15.0 15.0 4050.0 1.8 4.441440e-16 1.8 1.8 1.8 1.8 1.8 4050.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3742.0 0.828101 0.312903 6.212262e-06 0.863965 0.988976 0.999134 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 15.0 15.0 15.0 9651.0 1.8 2.220561e-16 1.8 1.8 1.8 1.8 1.8 9651.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 9040.0 0.811864 0.311791 2.103236e-10 0.792671 0.980873 0.998145 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 15.0 15.0 15.0 3914.0 1.8 4.441460e-16 1.8 1.8 1.8 1.8 1.8 3914.0 0.005 0.000000e+00 0.005 0.005 0.005 0.005 0.005 3721.0 0.843357 0.293269 1.672285e-07 0.876753 0.989395 0.998910 1.0
Spaceflight Aged
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7547.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5465.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5842.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 4922.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7539.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3595.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 5095.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3669.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 5747.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5265.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3864.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3672.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3730.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 1321.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 18386.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18386.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 4050.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 9651.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9651.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3914.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[18 rows x 232 columns]
Out[ ]:
| Group | sample | Sample Number | Characteristics[Organism] | Term Source REF | Term Accession Number | Characteristics[Strain] | Term Source REF.1 | Term Accession Number.1 | Characteristics[Genotype] | Term Source REF.2 | Term Accession Number.2 | Characteristics[Animal Source] | Characteristics[Sex] | Term Source REF.3 | Term Accession Number.3 | Factor Value[Spaceflight] | Term Source REF.4 | Term Accession Number.4 | Factor Value[Age] | Unit | Term Source REF.5 | Term Accession Number.5 | Characteristics[Material Type] | Term Source REF.6 | Term Accession Number.6 | Characteristics[diet] | Characteristics[Feeding Schedule] | Characteristics[Age at Euthanasia] | Unit.1 | Term Source REF.7 | Term Accession Number.7 | Protocol REF | Parameter Value[habitat] | Parameter Value[Enrichment material] | Parameter Value[duration] | Unit.2 | Term Source REF.8 | Term Accession Number.8 | Parameter Value[light cycle] | Protocol REF.1 | Parameter Value[Euthanasia Method] | Parameter Value[Sample Preservation Method] | Term Source REF.9 | Term Accession Number.9 | Parameter Value[Sample Storage Temperature] | Unit.3 | Term Source REF.10 | Term Accession Number.10 | Comment[RFID] | Comment[Euthanasia Date] | Time | n_cells_original_sample | kws_pp_sample | n_genes_by_counts | total_counts | log1p_n_genes_by_counts | log1p_total_counts | total_counts_mt | pct_counts_mt | log1p_total_counts_mt | total_counts_ribo | pct_counts_ribo | log1p_total_counts_ribo | total_counts_hb | pct_counts_hb | log1p_total_counts_hb | n_counts | n_genes | doublet_score | predicted_doublet | leiden_individual_res0.2dist1.5 | annotation_by_markers_individual_res0.2dist1.5 | annotation_by_markers_individual_heterogeneous_collapsed | leiden_individual | annotation_by_markers_individual | kws_cluster_individual | annotation_scanvi | kws_integrate | leiden | leiden_resolution | leiden_n_neighbors | leiden_min_dist | leiden_subcluster | resolution_leiden_subcluster | annotation_by_overlap | majority_voting_short | Age_Start | Age_End | Condition | annotation_toppgene | predicted_labels | majority_voting | majority_voting_probabilities | annotation_majority_voting | Spaceflight | Aged | annotation_scanvi_collapsed | annotation_scanvi_collapsed_hierarchy | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AAACAGCCAATCGCAC-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 20 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | HGC-ISS-04 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | 1149.0 | 8542 | {'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [4... | 2074 | 2981.067871 | 7.637716 | 8.000372 | 13.387463 | 0.449083 | 2.666357 | 10.547901 | 0.353830 | 2.446504 | 1.628919 | 0.054642 | 0.966573 | 4962.0 | 2152 | 0.030852 | False | 1 | Neuron | Neuron | 1 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,0 | 0.005 | Neuron | L4/5 IT CTX Glut | 12 Weeks | 20 Weeks | Ground Control | Excitatory | 006 L4/5 IT CTX Glut | 006 L4/5 IT CTX Glut | 0.842869 | Glutamatergic | 0 | 0 | Neuron | Neurons |
| AAACAGCCAGCACCAT-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 20 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | HGC-ISS-04 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | 1149.0 | 8542 | {'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [4... | 1568 | 2737.962158 | 7.358194 | 7.915334 | 13.935327 | 0.508967 | 2.703729 | 13.991938 | 0.511035 | 2.707513 | 1.490915 | 0.054453 | 0.912650 | 2994.0 | 1636 | 0.052691 | False | 13 | Neuron | Neuron | 13 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,0 | 0.005 | Neuron | PVT-PT Ntrk1 Glut | 12 Weeks | 20 Weeks | Ground Control | Excitatory | 149 PVT-PT Ntrk1 Glut | 149 PVT-PT Ntrk1 Glut | 0.813551 | Glutamatergic | 0 | 0 | Neuron | Neurons |
| AAACAGCCAGGTTCAC-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 20 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | HGC-ISS-04 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | 1149.0 | 8542 | {'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [4... | 2953 | 3375.157715 | 7.990915 | 8.124494 | 1.556682 | 0.046122 | 0.938710 | 8.833843 | 0.261731 | 2.285830 | 0.000000 | 0.000000 | 0.000000 | 8625.0 | 3080 | 0.014899 | False | 6 | Neuron | Neuron | 6 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,1 | 0.005 | Neuron | L6b CTX Glut | 12 Weeks | 20 Weeks | Ground Control | Excitatory | 029 L6b CTX Glut | 029 L6b CTX Glut | 0.337684 | Glutamatergic | 0 | 0 | Neuron | Neurons |
| AAACAGCCATCAATCG-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 20 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | HGC-ISS-04 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | 1149.0 | 8542 | {'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [4... | 1071 | 2224.446777 | 6.977281 | 7.707713 | 5.198409 | 0.233694 | 1.824293 | 9.021851 | 0.405577 | 2.304768 | 0.000000 | 0.000000 | 0.000000 | 2017.0 | 1116 | 0.018405 | False | 10 | Oligodendrocyte | Oligodendrocyte | 10 | Oligodendrocyte | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Oligodendrocyte | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 3 | 0.0075 | 15 | 1.8 | 3 | 0.005 | Oligodendrocyte | Oligo NN | 12 Weeks | 20 Weeks | Ground Control | Oligodendrocyte | 327 Oligo NN | 327 Oligo NN | 0.999630 | Oligodendrocyte | 0 | 0 | Oligodendrocyte | Macroglia |
| AAACAGCCATGTCAAT-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 20 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | HGC-ISS-04 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | 1149.0 | 8542 | {'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [4... | 2527 | 3241.278564 | 7.835184 | 8.084032 | 7.211684 | 0.222495 | 2.105558 | 9.091643 | 0.280496 | 2.311708 | 0.000000 | 0.000000 | 0.000000 | 6688.0 | 2614 | 0.004741 | False | 6 | Neuron | Neuron | 6 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,1 | 0.005 | Neuron | L6 CT CTX Glut | 12 Weeks | 20 Weeks | Ground Control | Excitatory | 030 L6 CT CTX Glut | 030 L6 CT CTX Glut | 0.995661 | Glutamatergic | 0 | 0 | Neuron | Neurons |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| TTTGTGTTCCCTGATC-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 20 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | FL-ISS-05 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | 885.0 | 4714 | {'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': ... | 3951 | 3733.228760 | 8.281977 | 8.225297 | 15.281790 | 0.409345 | 2.790047 | 32.756115 | 0.877420 | 3.519162 | 0.000000 | 0.000000 | 0.000000 | 12819.0 | 4151 | 0.006702 | False | 6 | Neuron | Neuron | 6 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,1 | 0.005 | Neuron | L6 CT CTX Glut | 12 Weeks | 20 Weeks | Space Flight | Excitatory | 030 L6 CT CTX Glut | 030 L6 CT CTX Glut | 0.659047 | Glutamatergic | 1 | 0 | Neuron | Neurons |
| TTTGTGTTCCGTAAAC-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 20 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | FL-ISS-05 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | 885.0 | 4714 | {'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': ... | 4371 | 3886.233643 | 8.382976 | 8.265453 | 10.151247 | 0.261210 | 2.411551 | 47.056725 | 1.210857 | 3.872382 | 0.505784 | 0.013015 | 0.409313 | 15502.0 | 4645 | 0.007560 | False | 5 | Neuron | Neuron | 5 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,2 | 0.005 | Neuron | CA1-ProS Glut | 12 Weeks | 20 Weeks | Space Flight | Excitatory | 016 CA1-ProS Glut | 016 CA1-ProS Glut | 0.028035 | Glutamatergic | 1 | 0 | Neuron | Neurons |
| TTTGTGTTCTGTGCAG-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 20 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | FL-ISS-05 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | 885.0 | 4714 | {'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': ... | 5541 | 3894.054932 | 8.620111 | 8.267463 | 17.510311 | 0.449668 | 2.918328 | 28.229233 | 0.724932 | 3.375169 | 0.000000 | 0.000000 | 0.000000 | 27623.0 | 5886 | 0.035294 | False | 1 | Neuron | Neuron | 1 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,0 | 0.005 | Neuron | L4/5 IT CTX Glut | 12 Weeks | 20 Weeks | Space Flight | Excitatory | 006 L4/5 IT CTX Glut | 006 L4/5 IT CTX Glut | 0.832242 | Glutamatergic | 1 | 0 | Neuron | Neurons |
| TTTGTTGGTGTTAGCA-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 20 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | FL-ISS-05 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | 885.0 | 4714 | {'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': ... | 845 | 1936.500610 | 6.740519 | 7.569154 | 24.934469 | 1.287604 | 3.255573 | 28.834591 | 1.489005 | 3.395669 | 0.000000 | 0.000000 | 0.000000 | 1469.0 | 873 | 0.007560 | False | 11 | Oligodendrocyte | Oligodendrocyte | 11 | Oligodendrocyte | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Oligodendrocyte | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 3 | 0.0075 | 15 | 1.8 | 3 | 0.005 | Oligodendrocyte | Oligo NN | 12 Weeks | 20 Weeks | Space Flight | Oligodendrocyte | 327 Oligo NN | 327 Oligo NN | 0.993962 | Oligodendrocyte | 1 | 0 | Oligodendrocyte | Macroglia |
| TTTGTTGGTTAAGTGT-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 20 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | FL-ISS-05 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | 885.0 | 4714 | {'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': ... | 3268 | 3615.137207 | 8.092239 | 8.193162 | 7.901684 | 0.218572 | 2.186240 | 28.523968 | 0.789015 | 3.385202 | 1.162011 | 0.032143 | 0.771039 | 9258.0 | 3411 | 0.058924 | False | 1 | Neuron | Neuron | 1 | Neuron | {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} | Neuron | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 0 | 0.0075 | 15 | 1.8 | 0,0 | 0.005 | Neuron | L4/5 IT CTX Glut | 12 Weeks | 20 Weeks | Space Flight | Excitatory | 006 L4/5 IT CTX Glut | 006 L4/5 IT CTX Glut | 0.964018 | Glutamatergic | 1 | 0 | Neuron | Neurons |
103274 rows × 99 columns
Data Descriptives¶
In [2]:
%matplotlib inline
# Detect Metadata Columns in AnnData Object
metadata_cols = self.rna.obs.groupby(col_sample).apply(
lambda x: list(x.columns[np.where(
x.apply(lambda y: len(y.unique()) == 1))[0]]))
metadata_cols = [x for x in metadata_cols.explode().unique() if all((
x in metadata_cols.loc[i] for i in metadata_cols.index.values))]
# Value Counts for Categorical Variables
metadata_cols_category = self.rna.obs[metadata_cols].dtypes[((
self.rna.obs[metadata_cols].dtypes == "category") | (self.rna.obs[
metadata_cols].apply(lambda x: len(x.unique()) < 6))) & (self.rna.obs[
metadata_cols].apply(lambda x: len(x.unique()) > 1))].index
for x in metadata_cols_category:
print(x, "\n\n", self.rna.obs[x].value_counts(), "\n\n")
if col_batch is not None: # by batch
if x != col_batch:
print(x, "\n\n", self.rna.obs[[x, col_batch]].groupby(
col_batch).value_counts(), "\n\n")
# Descriptives for Continuous Variables
print(self.rna.obs[self.rna.obs.columns.difference(
metadata_cols_category)].describe())
if col_batch is not None: # by batch
print(self.rna.obs[list(self.rna.obs.columns.difference(
metadata_cols_category)) + [col_batch]].groupby(col_batch).describe())
# Cells per Cluster
perc_clusters = round(100 * self.rna.obs.groupby(col_sample).value_counts(
[col_celltype], normalize=True).unstack(1), 2)
print(f"\n\n*** Cell Composition (%) ***\n\n{perc_clusters}")
# Number of Cells
n_cells = self.rna.obs.groupby(col_sample).apply(
lambda x: x.n_cells_original_sample.unique()[0],
include_groups=False).to_frame("Original Cell N").join(
self.rna.obs.groupby(col_sample).apply(
lambda x: x.shape[0], include_groups=False).to_frame("N Cells"))
n_cells.loc[:, "Percent_Filtered"] = round(100 * (1 - n_cells[
"N Cells"] / n_cells["Original Cell N"]), 2)
print("\n\n", n_cells.sort_values("Percent_Filtered"))
/tmp/ipykernel_919320/1416146960.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. metadata_cols = self.rna.obs.groupby(col_sample).apply(
Group
Group
Ground Control | 37 Weeks 35362
Ground Control | 20 Weeks 31600
Space Flight | 37 Weeks 19904
Space Flight | 20 Weeks 16408
Name: count, dtype: int64
sample
sample
RRRM2_BRN_GC_ISS-T_OLD_GO16 18386
RRRM2_BRN_GC_ISS-T_YNG_GY2 9651
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321
Name: count, dtype: int64
sample
Group sample
Ground Control | 20 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY2 9651
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672
RRRM2_BRN_GC_ISS-T_OLD_GO18 0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0
RRRM2_BRN_GC_ISS-T_OLD_GO19 0
RRRM2_BRN_GC_ISS-T_OLD_GO13 0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0
RRRM2_BRN_GC_ISS-T_OLD_GO16 0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 0
Ground Control | 37 Weeks RRRM2_BRN_GC_ISS-T_OLD_GO16 18386
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595
RRRM2_BRN_GC_ISS-T_YNG_GY4 0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 0
RRRM2_BRN_GC_ISS-T_YNG_GY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 0
RRRM2_BRN_GC_ISS-T_YNG_GY1 0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 0
RRRM2_BRN_GC_ISS-T_YNG_GY2 0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 0
Space Flight | 20 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669
RRRM2_BRN_GC_ISS-T_YNG_GY4 0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0
RRRM2_BRN_GC_ISS-T_OLD_GO18 0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0
RRRM2_BRN_GC_ISS-T_OLD_GO19 0
RRRM2_BRN_GC_ISS-T_OLD_GO13 0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 0
RRRM2_BRN_GC_ISS-T_YNG_GY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 0
RRRM2_BRN_GC_ISS-T_YNG_GY1 0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0
RRRM2_BRN_GC_ISS-T_OLD_GO16 0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 0
RRRM2_BRN_GC_ISS-T_YNG_GY2 0
Space Flight | 37 Weeks RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321
RRRM2_BRN_GC_ISS-T_YNG_GY4 0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0
RRRM2_BRN_GC_ISS-T_OLD_GO18 0
RRRM2_BRN_GC_ISS-T_OLD_GO19 0
RRRM2_BRN_GC_ISS-T_OLD_GO13 0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 0
RRRM2_BRN_GC_ISS-T_YNG_GY7 0
RRRM2_BRN_GC_ISS-T_YNG_GY1 0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 0
RRRM2_BRN_GC_ISS-T_OLD_GO16 0
RRRM2_BRN_GC_ISS-T_YNG_GY2 0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 0
Name: count, dtype: int64
Sample Number
Sample Number
HGC-ISS-16 18386
HGC-ISS-02 9651
HGC-ISS-04 7547
HGC-ISS-19 7539
HGC-ISS-18 5842
FL-ISS-19 5747
HGC-ISS-09 5465
HGC-ISS-07 5265
FL-ISS-08 5095
FL-ISS-20 4922
FL-ISS-16 4050
FL-ISS-05 3914
FL-ISS-14 3864
FL-ISS-02 3730
HGC-ISS-01 3672
FL-ISS-07 3669
HGC-ISS-13 3595
FL-ISS-17 1321
Name: count, dtype: int64
Sample Number
Group Sample Number
Ground Control | 20 Weeks HGC-ISS-02 9651
HGC-ISS-04 7547
HGC-ISS-09 5465
HGC-ISS-07 5265
HGC-ISS-01 3672
FL-ISS-02 0
FL-ISS-05 0
FL-ISS-07 0
FL-ISS-08 0
FL-ISS-14 0
FL-ISS-16 0
FL-ISS-17 0
FL-ISS-19 0
FL-ISS-20 0
HGC-ISS-13 0
HGC-ISS-16 0
HGC-ISS-18 0
HGC-ISS-19 0
Ground Control | 37 Weeks HGC-ISS-16 18386
HGC-ISS-19 7539
HGC-ISS-18 5842
HGC-ISS-13 3595
FL-ISS-02 0
FL-ISS-05 0
FL-ISS-07 0
FL-ISS-08 0
FL-ISS-14 0
FL-ISS-16 0
FL-ISS-17 0
FL-ISS-19 0
FL-ISS-20 0
HGC-ISS-01 0
HGC-ISS-02 0
HGC-ISS-04 0
HGC-ISS-07 0
HGC-ISS-09 0
Space Flight | 20 Weeks FL-ISS-08 5095
FL-ISS-05 3914
FL-ISS-02 3730
FL-ISS-07 3669
FL-ISS-14 0
FL-ISS-16 0
FL-ISS-17 0
FL-ISS-19 0
FL-ISS-20 0
HGC-ISS-01 0
HGC-ISS-02 0
HGC-ISS-04 0
HGC-ISS-07 0
HGC-ISS-09 0
HGC-ISS-13 0
HGC-ISS-16 0
HGC-ISS-18 0
HGC-ISS-19 0
Space Flight | 37 Weeks FL-ISS-19 5747
FL-ISS-20 4922
FL-ISS-16 4050
FL-ISS-14 3864
FL-ISS-17 1321
FL-ISS-02 0
FL-ISS-05 0
FL-ISS-07 0
FL-ISS-08 0
HGC-ISS-01 0
HGC-ISS-02 0
HGC-ISS-04 0
HGC-ISS-07 0
HGC-ISS-09 0
HGC-ISS-13 0
HGC-ISS-16 0
HGC-ISS-18 0
HGC-ISS-19 0
Name: count, dtype: int64
Factor Value[Spaceflight]
Factor Value[Spaceflight]
Ground Control 66962
Space Flight 36312
Name: count, dtype: int64
Factor Value[Spaceflight]
Group Factor Value[Spaceflight]
Ground Control | 20 Weeks Ground Control 31600
Space Flight 0
Ground Control | 37 Weeks Ground Control 35362
Space Flight 0
Space Flight | 20 Weeks Space Flight 16408
Ground Control 0
Space Flight | 37 Weeks Space Flight 19904
Ground Control 0
Name: count, dtype: int64
Term Source REF.4
Term Source REF.4
OSD 66962
MESH 36312
Name: count, dtype: int64
Term Source REF.4
Group Term Source REF.4
Ground Control | 20 Weeks OSD 31600
MESH 0
Ground Control | 37 Weeks OSD 35362
MESH 0
Space Flight | 20 Weeks MESH 16408
OSD 0
Space Flight | 37 Weeks MESH 19904
OSD 0
Name: count, dtype: int64
Term Accession Number.4
Term Accession Number.4
https://osdr.nasa.gov/ 66962
http://purl.bioontology.org/ontology/MESH/D013026 36312
Name: count, dtype: int64
Term Accession Number.4
Group Term Accession Number.4
Ground Control | 20 Weeks https://osdr.nasa.gov/ 31600
http://purl.bioontology.org/ontology/MESH/D013026 0
Ground Control | 37 Weeks https://osdr.nasa.gov/ 35362
http://purl.bioontology.org/ontology/MESH/D013026 0
Space Flight | 20 Weeks http://purl.bioontology.org/ontology/MESH/D013026 16408
https://osdr.nasa.gov/ 0
Space Flight | 37 Weeks http://purl.bioontology.org/ontology/MESH/D013026 19904
https://osdr.nasa.gov/ 0
Name: count, dtype: int64
Factor Value[Age]
Factor Value[Age]
29 55266
12 48008
Name: count, dtype: int64
Factor Value[Age]
Group Factor Value[Age]
Ground Control | 20 Weeks 12 31600
29 0
Ground Control | 37 Weeks 29 35362
12 0
Space Flight | 20 Weeks 12 16408
29 0
Space Flight | 37 Weeks 29 19904
12 0
Name: count, dtype: int64
Characteristics[Age at Euthanasia]
Characteristics[Age at Euthanasia]
37 55266
20 48008
Name: count, dtype: int64
Characteristics[Age at Euthanasia]
Group Characteristics[Age at Euthanasia]
Ground Control | 20 Weeks 20 31600
37 0
Ground Control | 37 Weeks 37 35362
20 0
Space Flight | 20 Weeks 20 16408
37 0
Space Flight | 37 Weeks 37 19904
20 0
Name: count, dtype: int64
Parameter Value[Sample Preservation Method]
Parameter Value[Sample Preservation Method]
Liquid Nitrogen 66962
Cryochiller 36312
Name: count, dtype: int64
Parameter Value[Sample Preservation Method]
Group Parameter Value[Sample Preservation Method]
Ground Control | 20 Weeks Liquid Nitrogen 31600
Cryochiller 0
Ground Control | 37 Weeks Liquid Nitrogen 35362
Cryochiller 0
Space Flight | 20 Weeks Cryochiller 16408
Liquid Nitrogen 0
Space Flight | 37 Weeks Cryochiller 19904
Liquid Nitrogen 0
Name: count, dtype: int64
Term Source REF.9
Term Source REF.9
NCIT 66962
OSD 36312
Name: count, dtype: int64
Term Source REF.9
Group Term Source REF.9
Ground Control | 20 Weeks NCIT 31600
OSD 0
Ground Control | 37 Weeks NCIT 35362
OSD 0
Space Flight | 20 Weeks OSD 16408
NCIT 0
Space Flight | 37 Weeks OSD 19904
NCIT 0
Name: count, dtype: int64
Term Accession Number.9
Term Accession Number.9
http://purl.obolibrary.org/obo/NCIT_C68796 66962
https://osdr.nasa.gov/ 36312
Name: count, dtype: int64
Term Accession Number.9
Group Term Accession Number.9
Ground Control | 20 Weeks http://purl.obolibrary.org/obo/NCIT_C68796 31600
https://osdr.nasa.gov/ 0
Ground Control | 37 Weeks http://purl.obolibrary.org/obo/NCIT_C68796 35362
https://osdr.nasa.gov/ 0
Space Flight | 20 Weeks https://osdr.nasa.gov/ 16408
http://purl.obolibrary.org/obo/NCIT_C68796 0
Space Flight | 37 Weeks https://osdr.nasa.gov/ 19904
http://purl.obolibrary.org/obo/NCIT_C68796 0
Name: count, dtype: int64
Comment[RFID]
Comment[RFID]
6E35413D61 18386
6E371E2032 9651
6E2A671967 7547
6E3C342F47 7539
6E3C216122 5842
6E3D2C2D2C 5747
6E3C705065 5465
6E28536840 5265
6E3C42091B 5095
6E272D285A 4922
6E27313758 4050
6E3E102A12 3914
6E2A180C12 3864
6E394B6C23 3730
6E28307F36 3672
6E3E325E7C 3669
6E3A3C1239 3595
6E353B735A 1321
Name: count, dtype: int64
Comment[RFID]
Group Comment[RFID]
Ground Control | 20 Weeks 6E371E2032 9651
6E2A671967 7547
6E3C705065 5465
6E28536840 5265
6E28307F36 3672
6E2A180C12 0
6E3A3C1239 0
6E3C342F47 0
6E3C42091B 0
6E3C216122 0
6E3D2C2D2C 0
6E3E102A12 0
6E3E325E7C 0
6E272D285A 0
6E353B735A 0
6E394B6C23 0
6E35413D61 0
6E27313758 0
Ground Control | 37 Weeks 6E35413D61 18386
6E3C342F47 7539
6E3C216122 5842
6E3A3C1239 3595
6E2A180C12 0
6E2A671967 0
6E3C42091B 0
6E3C705065 0
6E3D2C2D2C 0
6E3E102A12 0
6E3E325E7C 0
6E272D285A 0
6E353B735A 0
6E371E2032 0
6E394B6C23 0
6E28307F36 0
6E27313758 0
6E28536840 0
Space Flight | 20 Weeks 6E3C42091B 5095
6E3E102A12 3914
6E394B6C23 3730
6E3E325E7C 3669
6E2A180C12 0
6E2A671967 0
6E3A3C1239 0
6E3C342F47 0
6E3C216122 0
6E3C705065 0
6E3D2C2D2C 0
6E272D285A 0
6E353B735A 0
6E371E2032 0
6E28307F36 0
6E35413D61 0
6E27313758 0
6E28536840 0
Space Flight | 37 Weeks 6E3D2C2D2C 5747
6E272D285A 4922
6E27313758 4050
6E2A180C12 3864
6E353B735A 1321
6E2A671967 0
6E3A3C1239 0
6E3C342F47 0
6E3C42091B 0
6E3C216122 0
6E3C705065 0
6E3E102A12 0
6E3E325E7C 0
6E371E2032 0
6E394B6C23 0
6E28307F36 0
6E35413D61 0
6E28536840 0
Name: count, dtype: int64
Comment[Euthanasia Date]
Comment[Euthanasia Date]
20-Sep-2019 31767
18-Sep-2019 29999
19-Sep-2019 25100
17-Sep-2019 8764
16-Sep-2019 7644
Name: count, dtype: int64
Comment[Euthanasia Date]
Group Comment[Euthanasia Date]
Ground Control | 20 Weeks 18-Sep-2019 26135
19-Sep-2019 5465
16-Sep-2019 0
17-Sep-2019 0
20-Sep-2019 0
Ground Control | 37 Weeks 20-Sep-2019 31767
19-Sep-2019 3595
16-Sep-2019 0
17-Sep-2019 0
18-Sep-2019 0
Space Flight | 20 Weeks 17-Sep-2019 8764
16-Sep-2019 7644
18-Sep-2019 0
19-Sep-2019 0
20-Sep-2019 0
Space Flight | 37 Weeks 19-Sep-2019 16040
18-Sep-2019 3864
16-Sep-2019 0
17-Sep-2019 0
20-Sep-2019 0
Name: count, dtype: int64
kws_pp_sample
kws_pp_sample
{'min_max_genes': [201.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 10018.199999999983], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 18386
{'min_max_genes': [507.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [696.45, 23662.899999999972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 9651
{'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [414.0, 11122.175000000005], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 7547
{'min_max_genes': [666.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [942.0, 21190.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 7539
{'min_max_genes': [326.1, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [422.1, 13468.399999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5842
{'min_max_genes': [200, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 14724.999999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5747
{'min_max_genes': [703.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1006.1500000000001, 23784.40000000001], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5465
{'min_max_genes': [798.75, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1176.375, 28050.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5265
{'min_max_genes': [481.425, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [681.7, 27907.149999999994], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5095
{'min_max_genes': [628.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [895.0, 19711.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 4922
{'min_max_genes': [766.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1213.2, 36130.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 4050
{'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1300.3, 32231.825000000015], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3914
{'min_max_genes': [719.475, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1128.9, 33910.62499999997], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3864
{'min_max_genes': [700.2, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1087.2, 35338.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3730
{'min_max_genes': [923.4000000000001, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1489.7, 37456.77499999999], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3672
{'min_max_genes': [331.925, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [452.77500000000003, 13453.3], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3669
{'min_max_genes': [822.975, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1282.925, 23156.550000000007], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3595
{'min_max_genes': [316.3, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [376.3, 11766.900000000009], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 1321
Name: count, dtype: int64
kws_pp_sample
Group kws_pp_sample
Ground Control | 20 Weeks {'min_max_genes': [507.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [696.45, 23662.899999999972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 9651
{'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [414.0, 11122.175000000005], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 7547
{'min_max_genes': [703.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1006.1500000000001, 23784.40000000001], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5465
{'min_max_genes': [798.75, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1176.375, 28050.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5265
{'min_max_genes': [923.4000000000001, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1489.7, 37456.77499999999], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3672
{'min_max_genes': [200, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 14724.999999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [201.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 10018.199999999983], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [316.3, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [376.3, 11766.900000000009], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [326.1, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [422.1, 13468.399999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [331.925, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [452.77500000000003, 13453.3], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [481.425, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [681.7, 27907.149999999994], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [628.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [895.0, 19711.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [666.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [942.0, 21190.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [700.2, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1087.2, 35338.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [719.475, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1128.9, 33910.62499999997], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [766.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1213.2, 36130.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1300.3, 32231.825000000015], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [822.975, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1282.925, 23156.550000000007], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
Ground Control | 37 Weeks {'min_max_genes': [201.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 10018.199999999983], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 18386
{'min_max_genes': [666.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [942.0, 21190.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 7539
{'min_max_genes': [326.1, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [422.1, 13468.399999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5842
{'min_max_genes': [822.975, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1282.925, 23156.550000000007], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3595
{'min_max_genes': [200, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 14724.999999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [316.3, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [376.3, 11766.900000000009], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [414.0, 11122.175000000005], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [331.925, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [452.77500000000003, 13453.3], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [481.425, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [681.7, 27907.149999999994], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [507.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [696.45, 23662.899999999972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [628.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [895.0, 19711.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [700.2, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1087.2, 35338.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [703.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1006.1500000000001, 23784.40000000001], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [719.475, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1128.9, 33910.62499999997], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [766.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1213.2, 36130.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [798.75, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1176.375, 28050.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1300.3, 32231.825000000015], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [923.4000000000001, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1489.7, 37456.77499999999], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
Space Flight | 20 Weeks {'min_max_genes': [481.425, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [681.7, 27907.149999999994], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5095
{'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1300.3, 32231.825000000015], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3914
{'min_max_genes': [700.2, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1087.2, 35338.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3730
{'min_max_genes': [331.925, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [452.77500000000003, 13453.3], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3669
{'min_max_genes': [200, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 14724.999999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [201.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 10018.199999999983], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [316.3, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [376.3, 11766.900000000009], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [414.0, 11122.175000000005], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [326.1, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [422.1, 13468.399999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [507.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [696.45, 23662.899999999972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [628.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [895.0, 19711.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [666.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [942.0, 21190.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [703.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1006.1500000000001, 23784.40000000001], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [719.475, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1128.9, 33910.62499999997], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [766.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1213.2, 36130.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [798.75, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1176.375, 28050.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [822.975, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1282.925, 23156.550000000007], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [923.4000000000001, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1489.7, 37456.77499999999], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
Space Flight | 37 Weeks {'min_max_genes': [200, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 14724.999999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 5747
{'min_max_genes': [628.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [895.0, 19711.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 4922
{'min_max_genes': [766.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1213.2, 36130.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 4050
{'min_max_genes': [719.475, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1128.9, 33910.62499999997], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 3864
{'min_max_genes': [316.3, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [376.3, 11766.900000000009], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 1321
{'min_max_genes': [201.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 10018.199999999983], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [414.0, 11122.175000000005], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [326.1, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [422.1, 13468.399999999998], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [331.925, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [452.77500000000003, 13453.3], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [481.425, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [681.7, 27907.149999999994], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [507.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [696.45, 23662.899999999972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [666.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [942.0, 21190.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [700.2, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1087.2, 35338.0], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [703.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1006.1500000000001, 23784.40000000001], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [798.75, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1176.375, 28050.25], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1300.3, 32231.825000000015], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [822.975, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1282.925, 23156.550000000007], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
{'min_max_genes': [923.4000000000001, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1489.7, 37456.77499999999], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'} 0
Name: count, dtype: int64
kws_cluster_individual
kws_cluster_individual
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} 93932
{'resolution': 0.5, 'min_dist': 1.5, 'n_comps': 50} 5747
{'resolution': 0.075, 'min_dist': 1.5, 'n_comps': 50} 3595
Name: count, dtype: int64
kws_cluster_individual
Group kws_cluster_individual
Ground Control | 20 Weeks {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} 31600
{'resolution': 0.5, 'min_dist': 1.5, 'n_comps': 50} 0
{'resolution': 0.075, 'min_dist': 1.5, 'n_comps': 50} 0
Ground Control | 37 Weeks {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} 31767
{'resolution': 0.075, 'min_dist': 1.5, 'n_comps': 50} 3595
{'resolution': 0.5, 'min_dist': 1.5, 'n_comps': 50} 0
Space Flight | 20 Weeks {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} 16408
{'resolution': 0.5, 'min_dist': 1.5, 'n_comps': 50} 0
{'resolution': 0.075, 'min_dist': 1.5, 'n_comps': 50} 0
Space Flight | 37 Weeks {'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50} 14157
{'resolution': 0.5, 'min_dist': 1.5, 'n_comps': 50} 5747
{'resolution': 0.075, 'min_dist': 1.5, 'n_comps': 50} 0
Name: count, dtype: int64
Age_Start
Age_Start
29 Weeks 55266
12 Weeks 48008
Name: count, dtype: int64
Age_Start
Group Age_Start
Ground Control | 20 Weeks 12 Weeks 31600
29 Weeks 0
Ground Control | 37 Weeks 29 Weeks 35362
12 Weeks 0
Space Flight | 20 Weeks 12 Weeks 16408
29 Weeks 0
Space Flight | 37 Weeks 29 Weeks 19904
12 Weeks 0
Name: count, dtype: int64
Age_End
Age_End
37 Weeks 55266
20 Weeks 48008
Name: count, dtype: int64
Age_End
Group Age_End
Ground Control | 20 Weeks 20 Weeks 31600
37 Weeks 0
Ground Control | 37 Weeks 37 Weeks 35362
20 Weeks 0
Space Flight | 20 Weeks 20 Weeks 16408
37 Weeks 0
Space Flight | 37 Weeks 37 Weeks 19904
20 Weeks 0
Name: count, dtype: int64
Condition
Condition
Ground Control 66962
Space Flight 36312
Name: count, dtype: int64
Condition
Group Condition
Ground Control | 20 Weeks Ground Control 31600
Space Flight 0
Ground Control | 37 Weeks Ground Control 35362
Space Flight 0
Space Flight | 20 Weeks Space Flight 16408
Ground Control 0
Space Flight | 37 Weeks Space Flight 19904
Ground Control 0
Name: count, dtype: int64
Spaceflight
Spaceflight
0 66962
1 36312
Name: count, dtype: int64
Spaceflight
Group Spaceflight
Ground Control | 20 Weeks 0 31600
1 0
Ground Control | 37 Weeks 0 35362
1 0
Space Flight | 20 Weeks 1 16408
0 0
Space Flight | 37 Weeks 1 19904
0 0
Name: count, dtype: int64
Aged
Aged
1 55266
0 48008
Name: count, dtype: int64
Aged
Group Aged
Ground Control | 20 Weeks 0 31600
1 0
Ground Control | 37 Weeks 1 35362
0 0
Space Flight | 20 Weeks 0 16408
1 0
Space Flight | 37 Weeks 1 19904
0 0
Name: count, dtype: int64
Parameter Value[Sample Storage Temperature] Parameter Value[duration] Time doublet_score leiden_min_dist leiden_n_neighbors leiden_resolution log1p_n_genes_by_counts log1p_total_counts log1p_total_counts_hb log1p_total_counts_mt log1p_total_counts_ribo \
count 103274.0 103274.0 103274.000000 103274.000000 1.032740e+05 103274.0 1.032740e+05 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000
mean -80.0 53.0 973.638302 0.041344 1.800000e+00 15.0 7.500000e-03 7.417736 7.881183 0.260397 2.382938 2.902018
std 0.0 0.0 164.049552 0.044008 4.440914e-16 0.0 5.204196e-18 0.778077 0.360944 0.395849 0.573577 0.851048
min -80.0 53.0 650.000000 0.000939 1.800000e+00 15.0 7.500000e-03 5.407172 6.692673 0.000000 0.000000 0.000000
25% -80.0 53.0 870.000000 0.012658 1.800000e+00 15.0 7.500000e-03 6.786999 7.619952 0.000000 2.033528 2.411256
50% -80.0 53.0 1019.000000 0.026517 1.800000e+00 15.0 7.500000e-03 7.514800 7.977367 0.000000 2.417125 2.970734
75% -80.0 53.0 1107.000000 0.051769 1.800000e+00 15.0 7.500000e-03 8.114025 8.198943 0.560137 2.784262 3.545701
max -80.0 53.0 1210.000000 0.427957 1.800000e+00 15.0 7.500000e-03 8.866300 8.475933 2.865808 3.876799 5.215870
majority_voting_probabilities n_cells_original_sample n_counts n_genes n_genes_by_counts pct_counts_hb pct_counts_mt pct_counts_ribo resolution_leiden_subcluster total_counts total_counts_hb total_counts_mt total_counts_ribo
count 8.174000e+04 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 1.032740e+05 103274.000000 103274.000000 103274.000000 103274.000000
mean 8.004442e-01 8868.877617 6141.850098 2285.132850 2174.142524 0.014995 0.485460 0.829614 5.000000e-03 2808.240723 0.422456 11.600281 23.477568
std 3.244514e-01 5555.444146 6050.134766 1536.856773 1450.048991 0.033161 0.373723 0.614553 8.673659e-19 893.974487 0.739523 6.798219 17.732740
min 4.892193e-13 1493.000000 300.000000 228.000000 222.000000 0.000000 0.000000 0.000000 5.000000e-03 805.475098 0.000000 0.000000 0.000000
25% 7.655978e-01 4863.000000 1469.000000 928.000000 885.250000 0.000000 0.217165 0.393203 5.000000e-03 2037.464783 0.000000 6.640997 10.147949
50% 9.809476e-01 6503.000000 3866.000000 1918.000000 1834.000000 0.000000 0.389005 0.673524 5.000000e-03 2913.247192 0.000000 10.213576 18.506222
75% 9.984942e-01 10779.000000 9198.000000 3499.000000 3340.000000 0.020098 0.639635 1.106981 5.000000e-03 3636.102173 0.750913 15.187874 33.663980
max 1.000000e+00 20000.000000 37385.000000 7625.000000 7088.000000 0.962084 3.545085 8.041131 5.000000e-03 4796.899414 16.563232 47.269436 183.171951
Parameter Value[Sample Storage Temperature] Parameter Value[duration] Time doublet_score \
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min
Group
Ground Control | 20 Weeks 31600.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 31600.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 31600.0 1106.037310 77.118373 961.0 1082.0 1107.0 1149.0 1210.0 31600.0 0.026022 0.024241 0.000939
Ground Control | 37 Weeks 35362.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 35362.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 35362.0 1052.704655 39.889900 1019.0 1019.0 1019.0 1085.0 1139.0 35362.0 0.065153 0.058371 0.001629
Space Flight | 20 Weeks 16408.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 16408.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 16408.0 728.490005 89.844140 660.0 661.0 707.0 707.0 885.0 16408.0 0.027771 0.023537 0.001713
Space Flight | 37 Weeks 19904.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 19904.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 19904.0 825.057124 100.790958 650.0 704.0 869.0 870.0 915.0 19904.0 0.034561 0.032071 0.001516
leiden_min_dist leiden_n_neighbors leiden_resolution ... pct_counts_mt pct_counts_ribo \
25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean ... 75% max count mean std min 25%
Group ...
Ground Control | 20 Weeks 0.009311 0.020458 0.033229 0.200935 31600.0 1.8 4.440962e-16 1.8 1.8 1.8 1.8 1.8 31600.0 15.0 0.0 15.0 15.0 15.0 15.0 15.0 31600.0 0.0075 ... 0.448150 2.817153 31600.0 0.551147 0.303721 0.0 0.349949
Ground Control | 37 Weeks 0.019517 0.046690 0.098458 0.427957 35362.0 1.8 4.440955e-16 1.8 1.8 1.8 1.8 1.8 35362.0 15.0 0.0 15.0 15.0 15.0 15.0 15.0 35362.0 0.0075 ... 0.673306 3.545085 35362.0 0.575305 0.424046 0.0 0.280994
Space Flight | 20 Weeks 0.009819 0.022242 0.037975 0.200935 16408.0 1.8 4.441027e-16 1.8 1.8 1.8 1.8 1.8 16408.0 15.0 0.0 15.0 15.0 15.0 15.0 15.0 16408.0 0.0075 ... 0.677820 3.291983 16408.0 1.219632 0.526935 0.0 0.871887
Space Flight | 37 Weeks 0.012640 0.026172 0.045226 0.238307 19904.0 1.8 4.441004e-16 1.8 1.8 1.8 1.8 1.8 19904.0 15.0 0.0 15.0 15.0 15.0 15.0 15.0 19904.0 0.0075 ... 0.881699 3.462019 19904.0 1.402012 0.757490 0.0 0.865775
resolution_leiden_subcluster total_counts total_counts_hb \
50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75%
Group
Ground Control | 20 Weeks 0.483076 0.682583 5.470688 31600.0 0.005 0.0 0.005 0.005 0.005 0.005 0.005 31600.0 2953.787842 798.679749 1018.616394 2250.032227 3126.676025 3657.309753 4613.192871 31600.0 0.425118 0.705483 0.0 0.0 0.0 0.773112
Ground Control | 37 Weeks 0.463607 0.800191 6.749294 35362.0 0.005 0.0 0.005 0.005 0.005 0.005 0.005 35362.0 2466.579346 903.588867 805.475098 1692.396820 2325.938477 3256.262817 4643.960449 35362.0 0.347006 0.732018 0.0 0.0 0.0 0.000000
Space Flight | 20 Weeks 1.140313 1.465173 7.849742 16408.0 0.005 0.0 0.005 0.005 0.005 0.005 0.005 16408.0 3084.272949 886.075806 1048.266235 2296.486633 3326.058594 3859.608948 4713.582520 16408.0 0.491993 0.748583 0.0 0.0 0.0 0.847184
Space Flight | 37 Weeks 1.233290 1.743376 8.041131 19904.0 0.005 0.0 0.005 0.005 0.005 0.005 0.005 19904.0 2956.621094 842.863586 841.438477 2152.055481 3264.747681 3681.248230 4796.899414 19904.0 0.494954 0.783714 0.0 0.0 0.0 0.851087
total_counts_mt total_counts_ribo
max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
Group
Ground Control | 20 Weeks 16.016462 31600.0 8.755315 5.311935 0.00000 4.922410 7.545821 11.342984 38.111645 31600.0 16.063892 9.233896 0.0 10.094914 14.208097 19.950861 122.755669
Ground Control | 37 Weeks 14.150034 35362.0 9.977168 5.348376 0.00000 6.191418 9.340921 12.932923 38.388371 35362.0 15.267353 13.528916 0.0 5.782949 10.543948 21.744246 153.015076
Space Flight | 20 Weeks 14.123777 16408.0 14.411705 6.304240 1.30298 9.522820 13.322799 18.381306 41.757008 16408.0 37.125362 17.822294 0.0 24.397658 35.151552 47.261961 183.171951
Space Flight | 37 Weeks 16.563232 19904.0 16.683048 7.844715 0.00000 10.419192 15.606552 22.051781 47.269436 19904.0 38.583508 17.900896 0.0 25.322685 36.105711 48.775124 166.185165
[4 rows x 200 columns]
*** Cell Composition (%) ***
annotation_scanvi_collapsed Astrocyte Endothelial Microglial Neuron OPC Oligodendrocyte
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 7.23 2.76 3.47 63.04 7.31 16.18
RRRM2_BRN_GC_ISS-T_YNG_GY9 9.13 1.32 3.37 65.51 3.44 17.24
RRRM2_BRN_GC_ISS-T_OLD_GO18 8.63 0.84 3.42 70.23 3.01 13.87
RRRM2_BRN_FLT_ISS-T_OLD_FO20 6.89 1.30 3.64 58.80 3.51 25.86
RRRM2_BRN_GC_ISS-T_OLD_GO19 6.53 0.77 3.41 67.28 3.46 18.56
RRRM2_BRN_GC_ISS-T_OLD_GO13 9.65 1.78 3.12 54.24 3.64 27.57
RRRM2_BRN_FLT_ISS-T_YNG_FY8 8.24 0.57 5.61 57.76 4.38 23.43
RRRM2_BRN_FLT_ISS-T_YNG_FY7 6.62 0.11 4.36 56.85 3.49 28.56
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5.31 1.20 3.74 73.99 2.44 13.33
RRRM2_BRN_GC_ISS-T_YNG_GY7 9.69 1.14 3.25 58.27 4.03 23.63
RRRM2_BRN_FLT_ISS-T_OLD_FO14 7.89 0.03 4.06 60.90 3.83 23.29
RRRM2_BRN_GC_ISS-T_YNG_GY1 8.63 1.03 3.87 61.96 3.70 20.81
RRRM2_BRN_FLT_ISS-T_YNG_FY2 7.64 0.03 4.42 59.73 3.81 24.37
RRRM2_BRN_FLT_ISS-T_OLD_FO17 2.65 0.00 1.97 80.32 4.84 10.22
RRRM2_BRN_GC_ISS-T_OLD_GO16 3.64 0.03 1.22 86.82 1.46 6.83
RRRM2_BRN_FLT_ISS-T_OLD_FO16 7.28 0.02 4.25 66.20 3.21 19.04
RRRM2_BRN_GC_ISS-T_YNG_GY2 7.58 1.34 3.08 59.28 3.79 24.93
RRRM2_BRN_FLT_ISS-T_YNG_FY5 7.28 0.51 3.83 67.17 3.76 17.45
Original Cell N N Cells Percent_Filtered
sample
RRRM2_BRN_GC_ISS-T_OLD_GO13 3880 3595 7.35
RRRM2_BRN_GC_ISS-T_YNG_GY1 3968 3672 7.46
RRRM2_BRN_GC_ISS-T_OLD_GO16 20000 18386 8.07
RRRM2_BRN_GC_ISS-T_YNG_GY7 5776 5265 8.85
RRRM2_BRN_FLT_ISS-T_OLD_FO14 4260 3864 9.30
RRRM2_BRN_GC_ISS-T_YNG_GY9 6044 5465 9.58
RRRM2_BRN_FLT_ISS-T_YNG_FY2 4125 3730 9.58
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5658 5095 9.95
RRRM2_BRN_GC_ISS-T_OLD_GO18 6503 5842 10.16
RRRM2_BRN_GC_ISS-T_YNG_GY2 10779 9651 10.46
RRRM2_BRN_GC_ISS-T_OLD_GO19 8476 7539 11.05
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1493 1321 11.52
RRRM2_BRN_GC_ISS-T_YNG_GY4 8542 7547 11.65
RRRM2_BRN_FLT_ISS-T_OLD_FO20 5641 4922 12.75
RRRM2_BRN_FLT_ISS-T_YNG_FY7 4238 3669 13.43
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4863 4050 16.72
RRRM2_BRN_FLT_ISS-T_YNG_FY5 4714 3914 16.97
RRRM2_BRN_FLT_ISS-T_OLD_FO19 7265 5747 20.89
Get Marker Gene Sets¶
From Google Drive, except senmayo derived from file downloaded from https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/SAUL_SEN_MAYO.html then created using:
from pybiomart import Server
senmayo = pd.read_csv("resources/SAUL_SEN_MAYO.v2025.1.Hs.tsv",
sep="\t", header=0)
senmayo = senmayo[senmayo.STANDARD_NAME == "GENE_SYMBOLS"]
senmayo = pd.Series({"Senmayo": senmayo.iloc[0, 1].split(
",")}, name="symbol").rename_axis("Gene_Set").explode().str.strip(
).replace("", np.nan).dropna().to_frame()
server = Server(host="http://www.ensembl.org")
mart = server.marts["ENSEMBL_MART_ENSEMBL"] # Access the Ensembl mart
human_dataset = mart.datasets["hsapiens_gene_ensembl"]
orthologs = human_dataset.query(attributes=[
"external_gene_name", "mmusculus_homolog_associated_gene_name"])
orthologs = orthologs.dropna().set_index(orthologs.columns[0])
orthologs = orthologs.loc[orthologs.index.intersection(
senmayo.symbol.to_list())].iloc[:, 0]
print(f"{orthologs.shape[0]} orthologs found out of"
f" {len(senmayo.symbol.unique())} senmayo genes")
senmayo = senmayo.assign(symbol=senmayo.replace({"symbol": dict(zip(
orthologs.index, orthologs.values))}))
senmayo.to_csv("gene_sets/senmayo.csv")
(Also could use scflow.tl.convert_gene_species(senmayo.symbol.to_list()))
Certain gene sets created from Google Drive ADBR AWG > Projects > White Matter > Gene Sets files (with the suffix = "_adjusted") were modified for consistency, e.g.,
pd.read_csv("gene_sets/_iron_genes_gmt_out.csv", index_col=[
0, 1]).rename_axis(["row_id", "pathway"]).stack().to_frame(
"symbol").reset_index(-1, drop=True).reset_index().to_csv(
"gene_sets/_iron_genes_gmt_out_adjusted.csv")
A Priori¶
In [3]:
mks = [pd.read_csv(os.path.join("gene_sets", i)).dropna(
how="all", axis=1).assign(Source_File=i) for i in os.listdir("gene_sets")]
mks = [x.assign(Gene_Set=x["Source_File"].iloc[0].split("_2025")[0]) if (
"pathway" in x) else x for x in mks]
mks = pd.concat(mks).drop("Unnamed: 0", axis=1).drop("row_id", axis=1)
mks = mks[mks.symbol.isin(self.rna.var_names)]
mks = mks[["Gene_Set", "symbol"]].set_index("Gene_Set").groupby(
"Gene_Set").apply(lambda x: x["symbol"].to_list())
marker_genes_dict = dict(mks)
marker_gene_sets = mks.copy()
print(marker_gene_sets)
Gene_Set GOBP_CELLULAR_RESPONSE_TO_IRON_ION [B2m, Bmp6, Gpld1, Ireb2, Tfrc, Trf] GOBP_CENTRAL_NERVOUS_SYSTEM_MYELIN_FORMATION [Abca2, Ckap5, Cntn1, Ercc2, Mag, Mios, Tenm4] GOBP_INTRACELLULAR_IRON_ION_HOMEOSTASIS [Abcb7, Aco1, Atp13a2, Atp6ap1, Atp6v0a2, Atp6v0d1, Atp6v1a, Atp6v1g1, B2m, Bmp6, Bmyc, Bola2, C... GOBP_IRON_COORDINATION_ENTITY_TRANSPORT [Abcb7, Abcc5, Flvcr1, Pgrmc2, Slc22a17, Slc48a1] GOBP_IRON_ION_IMPORT_ACROSS_PLASMA_MEMBRANE [Iscu, Steap2] GOBP_IRON_ION_TRANSMEMBRANE_TRANSPORT [Abcb7, Abcc5, Atp7a, Hif1a, Iscu, Nos1, Slc11a2, Slc25a28, Slc25a37, Slc39a14, Slc48a1, Steap2] GOBP_IRON_ION_TRANSPORT [Abcb7, Abcc5, Arhgap1, Atp7a, B2m, Cltc, Dnm2, Flvcr1, Fth1, Heph, Hif1a, Iscu, Lmtk2, Mmgt1, M... GOBP_MULTICELLULAR_ORGANISMAL_LEVEL_IRON_ION_HOMEOSTASIS [Ank1, B2m, Bmp6, Btbd9, Eif2ak1, Epas1, Fbxl5, Fech, Htt, Ireb2, Neo1, Picalm, Slc11a2, Sod2] GOBP_MYELIN_ASSEMBLY [Abca2, Cd9, Ckap5, Cntn1, Cntnap1, Dicer1, Epb41l3, Ercc2, Fig4, Gnpat, Gpc1, Ilk, Mag, Mios, M... GOBP_MYELIN_MAINTENANCE [Abcd1, Abcd2, Akt1, Akt2, Clu, Degs1, Epb41l3, Fa2h, Myrf, Ndrg1, Pals1, Plec, Pten, Sh3tc2, Sod1] GOBP_NEGATIVE_REGULATION_OF_IRON_ION_TRANSMEMBRANE_TRANSPORT [Atp7a, Iscu, Nos1] GOBP_NEGATIVE_REGULATION_OF_IRON_ION_TRANSPORT [Atp7a, Iscu, Nos1] GOBP_REGULATION_OF_IRON_ION_TRANSMEMBRANE_TRANSPORT [Atp7a, Iscu, Nos1] GOBP_REGULATION_OF_IRON_ION_TRANSPORT [Atp7a, B2m, Iscu, Nos1, Trf] GOBP_REGULATION_OF_MYELINATION [Akt1, Cdh2, Ctnnb1, Cyfip1, Dag1, Dicer1, Dlg1, Eif2ak3, Fig4, Hes5, Hgf, Hnrnpk, Igf1, Jam2, L... GOBP_RESPONSE_TO_IRON_ION [Abat, Aco1, B2m, Bcl2, Bmp6, Gpld1, Ireb2, Snca, Tfrc, Trf] GOBP_SEQUESTERING_OF_IRON_ION [Fth1, Ftl1] GOBP_SPHINGOMYELIN_BIOSYNTHETIC_PROCESS [Abca8a, Abca8b, Ormdl3, Osbp, Samd8, Sgms1, Sptlc1, Sptlc2, Vapa] GOBP_SPHINGOMYELIN_CATABOLIC_PROCESS [Prkcd, Smpd1, Smpd3, Smpd4, Smpdl3a, Smpdl3b] GOBP_SPHINGOMYELIN_METABOLIC_PROCESS [Abca2, Abca8a, Abca8b, Ormdl3, Osbp, Prkcd, Samd8, Sgms1, Smpd1, Smpd3, Smpd4, Smpdl3a, Smpdl3b... GOMF_FERRIC_IRON_BINDING [Fth1, Ftl1, Trf] GOMF_FERROUS_IRON_BINDING [Alkbh1, Alkbh3, Cdo1, Dnajc24, Egln1, Egln2, Fth1, Ftl1, Fto, Heph, Hif1an, Iscu, Nt5e, Phyh, P... GOMF_FERROUS_IRON_TRANSMEMBRANE_TRANSPORTER_ACTIVITY [Mmgt1, Slc11a2, Slc25a28, Slc25a37, Slc39a14] GOMF_IRON_ION_BINDING [Abce1, Aco2, Ado, Agmo, Alkbh1, Alkbh3, Alkbh8, Calr, Cdo1, Ciapin1, Cisd1, Cygb, Cyp11a1, Cyp2... GOMF_IRON_ION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY [Mmgt1, Slc11a2, Slc25a28, Slc25a37, Slc39a14, Tfrc] GOMF_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_PAIRED_DONORS_WITH_INCORPORATION_OR_REDUCTION_OF_MOLECULAR_OXYGEN_REDUCED_IRON_SULFUR_PROTEIN_AS_ONE_DONOR_AND_INCORPORATION_OF_ONE_ATOM_OF_OXYGEN [Ahr, Cyp11a1, Cyp2u1, Fdx1] GOMF_SPHINGOMYELIN_PHOSPHODIESTERASE_ACTIVITY [Smpd1, Smpd3, Smpd4, Smpdl3a, Smpdl3b] GOMF_STRUCTURAL_CONSTITUENT_OF_MYELIN_SHEATH [Gpm6b, Mal, Mal2, Mbp, Mobp, Pllp, Plp1] REACTOME_IRON_UPTAKE_AND_TRANSPORT [Aco1, Atp6ap1, Atp6v0a1, Atp6v0a2, Atp6v0a4, Atp6v0b, Atp6v0c, Atp6v0d1, Atp6v0e, Atp6v0e2, Atp... REACTOME_MITOCHONDRIAL_IRON_SULFUR_CLUSTER_BIOGENESIS [Fdx1, Fdxr, Glrx5, Hscb, Isca1, Isca2, Iscu, Lyrm4, Nfs1] Senmayo [Acvr1b, Angpt1, Bex3, Bmp6, Cd9, Csf1, Ctnnb1, Ctsb, Egf, Egfr, Ets2, Fgf1, Hgf, Hmgb1, Igf1, I... WP_IRON_HOMEOSTASIS [Fth1, Ftl1, Ireb2, Trf] dtype: object
Clusters in Data¶
In [4]:
self.find_markers(col_celltype=col_celltype)
df_celltype_markers = self.get_markers_df(
n_genes=50, col_celltype=col_celltype,
log2fc_threshold=1, p_threshold=1e-10)
df_celltype_markers.groupby(col_celltype).apply(lambda x: x.head(3))
Out[4]:
| scores | logfoldchanges | pvals | pvals_adj | |||
|---|---|---|---|---|---|---|
| annotation_scanvi_collapsed | annotation_scanvi_collapsed | names | ||||
| Astrocyte | Astrocyte | Bcl2 | 48.966312 | 2.244818 | 0.000000e+00 | 0.000000e+00 |
| Abhd3 | 47.107872 | 3.003248 | 0.000000e+00 | 0.000000e+00 | ||
| Ugp2 | 47.243469 | 2.251395 | 0.000000e+00 | 0.000000e+00 | ||
| Endothelial | Endothelial | Zbtb20 | 71.523643 | 3.209239 | 0.000000e+00 | 0.000000e+00 |
| Cped1 | 37.842743 | 7.205899 | 3.891507e-186 | 6.621035e-185 | ||
| 9530026P05Rik | 34.913925 | 5.121674 | 7.849119e-168 | 1.249353e-166 | ||
| Microglial | Microglial | Tgfbr1 | 151.537048 | 7.483766 | 0.000000e+00 | 0.000000e+00 |
| Hexb | 150.142044 | 5.966863 | 0.000000e+00 | 0.000000e+00 | ||
| Plxdc2 | 145.446838 | 4.734621 | 0.000000e+00 | 0.000000e+00 | ||
| Neuron | Neuron | Pde7a | 56.121979 | 1.154942 | 0.000000e+00 | 0.000000e+00 |
| Chic1 | 55.948746 | 1.314887 | 0.000000e+00 | 0.000000e+00 | ||
| Pkig | 55.969212 | 1.236063 | 0.000000e+00 | 0.000000e+00 | ||
| OPC | OPC | Lhfpl3 | 161.057190 | 5.147624 | 0.000000e+00 | 0.000000e+00 |
| Tnr | 131.139984 | 4.126481 | 0.000000e+00 | 0.000000e+00 | ||
| Sox6 | 118.223061 | 5.241720 | 0.000000e+00 | 0.000000e+00 | ||
| Oligodendrocyte | Oligodendrocyte | Map4k5 | 52.164612 | 1.264680 | 0.000000e+00 | 0.000000e+00 |
| Dleu2 | 54.534840 | 1.288954 | 0.000000e+00 | 0.000000e+00 | ||
| E130308A19Rik | 54.438168 | 1.582712 | 0.000000e+00 | 0.000000e+00 |
Regress Out?¶
In [5]:
# if vars_regress_out is not None:
# self.rna.X = self.rna.layers["log1p"].copy()
# sc.pp.regress_out(self.rna, vars_regress_out)
# self.rna.layers["log1p"] = self.rna.X.copy()
# pkg.pp.scale(self.rna, zero_center=True, max_value=10)
# self.rna.layers["scaled"] = self.rna.X.copy()
# self.rna.X = self.rna.layers["log1p"].copy()
Score Senescence¶
Run Scoring¶
If you want just to find the senescence-related genes, run:
tissue = "Brain" # or whatever you want here
celltype = None # or whatever you want here
genes_senepy, hubs, figs = find_senescence_genes(
self.rna, species=species, tissue=tissue, celltype=celltype,
overlap_threshold=0, literature_sources=None,
sengpt_sources=True, plot=True, col_celltype=col_celltype
) # find tissue- and/or cell type-specific genes
To see available hubs:
hubs = senepy.load_hubs(species=species)
hubs.metadata.set_index(["tissue", "cell"]).sort_index()
Other examples of arguments you could use:
# tissue = ["Brain", "Myeloid"]
# tissue = None
# celltype = "GABA interneuron" # only human I think
# celltype = ["microglia", "t cell", "monocyte"]
# celltype = "microglia"
# celltype = "microglial cell"`
Senepy¶
In [6]:
%%time
tissue = "Brain"
celltype = None # use all cell types
if "senepy" in sen_metrics:
self.rna.X = self.rna.layers["log1p"].copy()
self.rna, genes_senepy, figs = run_senepy(
self.rna, species=species, tissue=tissue, celltype=celltype,
overlap_threshold=0, literature_sources=None,
sengpt_sources=True, col_celltype=col_celltype,
col_senscore="score_senepy", identifiers=[col_celltype],
use_translator=True, plot=True, figsize=(20, 20),
swap_axes=False, standard_scale="group", plot_layer="log1p"
) # find tissue- and/or cell type-specific genes
self.rna.obs.loc[
:, "score_senepy_original_scale"] = self.rna.obs.score_senepy
self.rna.obs.loc[:, "score_senepy"] = np.log(
self.rna.obs.score_senepy) # log-transform senescence score
self.rna.obs[["score_senepy"]]
CPU times: user 2 μs, sys: 1e+03 ns, total: 3 μs Wall time: 5.72 μs
Senmayo¶
In [7]:
if "senmayo" in sen_metrics:
sc.tl.score_genes(self.rna, marker_gene_sets.loc["Senmayo"],
ctrl_as_ref=True, ctrl_size=50, gene_pool=None,
n_bins=25, score_name="score_senmayo", random_state=0,
copy=False, layer="log1p", use_raw=False)
Label SnCs¶
Define Snc Threshold & Label Cells¶
Based on percentile threshold derived from control group
In [8]:
# Determine Threshold
perc_s, perc = str(100 - percentile) + "%", 1 - (percentile / 100)
p_h = 3
perc_s_h, perc_h = str(100 - p_h) + "%", 1 - (p_h / 100)
print("Percentile for Threshold:", perc_s)
for u in sen_metrics:
self.rna.obs = self.rna.obs.join(self.rna.obs[self.rna.obs[
col_batch] == keys[col_batch]["key_control"]].groupby(
col_celltype).apply(lambda x: x[f"score_{u}"].describe(
percentiles=[perc])[perc_s], include_groups=False).to_frame(
f"Senescence_Threshold_{u}"), on=col_celltype).loc[
self.rna.obs.index]
self.rna.obs = self.rna.obs.join(self.rna.obs[self.rna.obs[
col_batch] == keys[col_batch]["key_control"]].groupby(
col_celltype).apply(lambda x: x[f"score_{u}"].describe(
percentiles=[perc_h])[perc_s_h],
include_groups=False).to_frame(
f"Senescence_Threshold_{u}_{p_h}"),
on=col_celltype).loc[self.rna.obs.index]
# Binary CLassification
for x in sen_metrics:
self.rna.obs.loc[:, f"Senescent_Cell_{x}"] = (self.rna.obs[
f"score_{x}"] >= self.rna.obs[f"Senescence_Threshold_{x}"])
self.rna.obs.loc[:, f"Senescent_Cell_{x}_{p_h}"] = (self.rna.obs[
f"score_{x}"] >= self.rna.obs[f"Senescence_Threshold_{x}_{p_h}"])
# String Labels (for Plotting/Categorical Analysis)
for x in sen_metrics:
self.rna.obs.loc[:, f"Senescent_Cell_Label_{x}"] = self.rna.obs[
f"Senescent_Cell_{x}"].astype(bool).astype(str).replace({
"True": "Senescent", "False": "Non-Senescent"})
self.rna.obs.loc[:, f"Senescent_Cell_Label_hierarchy_{x}"] = self.rna.obs[
f"Senescent_Cell_{x}"].astype(bool).astype(str).replace({
"True": "Senescent", "False": "Non-Senescent"})
self.rna.obs.loc[:, f"Senescent_Cell_Label_{x}_{p_h}"] = self.rna.obs[
f"Senescent_Cell_{x}_{p_h}"].astype(bool).astype(str).replace({
"True": "Senescent", "False": "Non-Senescent"})
self.rna.obs.loc[:, f"Senescent_Cell_Label_by_Type_{x}"] = self.rna.obs[
f"Senescent_Cell_Label_{x}"].replace({
"Senescent": "SnC ", "Non-Senescent": ""}) + self.rna.obs[
col_celltype].astype(str)
self.rna.obs.loc[:, f"SnC_hierarchy_{x}"] = self.rna.obs[
f"Senescent_Cell_Label_{x}"].replace({
"Senescent": "SnC ", "Non-Senescent": ""}) + self.rna.obs[
col_celltype + "_hierarchy"].astype(str)
# Descriptives
for x in sen_metrics:
print(x, round(self.rna.obs[[
f"Senescent_Cell_Label_{x}", col_celltype]].value_counts(
normalize=True).sort_index() * 100, 1).unstack(0))
# Choose Metrics for Main Analysis
for x in ["Senescent_Cell_Label", "Senescent_Cell", "SnC_hierarchy",
"Senescence_Threshold", "Senescent_Cell_Label_by_Type"]:
self.rna.obs.loc[:, x] = self.rna.obs[f"{x}_{use_metric}"]
self.rna.obs.loc[:, "senscore"] = self.rna.obs[f"score_{use_metric}"]
self.rna.obs.loc[
:, f"Senescent_Cell_Label_by_Type_{use_metric}_{p_h}"] = self.rna.obs[
f"Senescent_Cell_Label_{use_metric}_{p_h}"].replace({
"Non-Senescent": "", "Senescent": "SnC "}).astype(
str) + self.rna.obs[
col_celltype].astype(str) # lower threshold SnC by type
genes = marker_gene_sets.loc["Senmayo"] if (
use_metric == "senmayo") else genes_senepy # main senescence gene list
# Avoid Nesting Duplication Issues
self.rna.obs.loc[:, "SnC_hierarchy"] = self.rna.obs[
"SnC_hierarchy"].replace({
"Neuron": "Neurons", "SnC Neuron": "SnC Neurons"})
Percentile for Threshold: 99% senmayo Senescent_Cell_Label_senmayo Non-Senescent Senescent annotation_scanvi_collapsed Astrocyte 6.8 0.1 Endothelial 0.8 0.0 Microglial 3.2 0.1 Neuron 65.9 1.5 OPC 3.4 0.0 Oligodendrocyte 17.9 0.2
Label Batch/Cell-Specific Senescence Percentiles¶
In [9]:
# for x in ["Senescence_Label", "Senescence_CellType"]:
# if x in self.rna.obs:
# self.rna.obs = self.rna.obs.drop(x, axis=1)
# # %ile Thresholds (Overall)
# thresh = self.rna.obs["score_senepy"].describe(
# percentiles=[0.90, 0.95, 0.99])
# thres_ct = self.rna.obs[["score_senepy", col_celltype]].groupby(
# col_celltype).describe(percentiles=[0.90, 0.95, 0.99])["score_senepy"]
# sen = self.rna.obs["score_senepy"].apply(
# lambda x: "Top 1%" if x >= float(thresh.loc["99%"]) else "Top 5%" if (
# x >= float(thresh.loc["95%"])) else "Top 10%" if x >= float(
# thresh.loc["90%"]) else "Bottom 90%").to_frame(
# "Senescence_Label").loc[self.rna.obs.index]
# self.rna.obs = self.rna.obs.join(sen).loc[self.rna.obs.index]
# # %ile Thresholds (by Cell Type)
# sct = self.rna.obs.groupby(
# col_celltype).apply(lambda g: g["score_senepy"].apply(
# lambda x: f"Top 1% {g.name}" if x >= float(thres_ct.loc[g.name][
# "99%"]) else f"Top 5% {g.name}" if (x >= float(thres_ct.loc[
# g.name]["95%"])) else f"Top 10% {g.name}" if x >= float(
# thres_ct.loc[g.name]["90%"]) else g.name),
# include_groups=False)
# sct = sct.to_frame("Senescence_CellType").reset_index(0, drop=True)
# sct = sct.loc[self.rna.obs.index]
# self.rna.obs = self.rna.obs.join(sct).loc[self.rna.obs.index]
# for x in ["Senescence_Label", "Senescence_CellType"]:
# self.rna.obs = self.rna.obs.assign(**{
# x: self.rna.obs[x].astype("category")})
# high_sen_cts = [i for i in self.rna.obs["Senescence_CellType"].unique(
# ) if " 1% " in i]
Descriptives of SnCs¶
In [10]:
print("Percent Senescent: \n\n", round(self.rna.obs.groupby([
col_celltype, col_batch]).apply(lambda x: x["Senescent_Cell"].astype(
int).value_counts(normalize=True), include_groups=False) * 100,
2).unstack(col_celltype))
sns.catplot(self.rna.obs, y="Senescent_Cell", x=col_celltype,
hue=col_batch, kind="bar", height=10)
Percent Senescent:
annotation_scanvi_collapsed Astrocyte Endothelial Microglial Neuron OPC Oligodendrocyte
Group Senescent_Cell
Ground Control | 20 Weeks 0 98.96 98.82 98.96 98.99 98.97 99.00
1 1.04 1.18 1.04 1.01 1.03 1.00
Ground Control | 37 Weeks 0 98.41 96.59 97.73 97.23 98.92 99.01
1 1.59 3.41 2.27 2.77 1.08 0.99
Space Flight | 20 Weeks 0 98.22 100.00 97.90 98.18 98.59 98.33
1 1.78 NaN 2.10 1.82 1.41 1.67
Space Flight | 37 Weeks 0 96.87 97.78 97.60 96.63 97.71 98.34
1 3.13 2.22 2.40 3.37 2.29 1.66
Out[10]:
<seaborn.axisgrid.FacetGrid at 0x70d63fa56510>
SnC DEGs¶
Overall¶
In [11]:
p_threshold = 1e-10
lfc_threshold = 1
self.find_markers(col_celltype="Senescent_Cell_Label_by_Type")
df_snc_markers = {}
df_snc_markers["All"] = self.get_markers_df(
n_genes=50, col_celltype="Senescent_Cell_Label_by_Type",
log2fc_threshold=1, p_threshold=1e-10)
# print(df_snc_markers["All"].loc[pd.unique([
# i[0] for i in df_snc_markers["All"].index.values if (
# "SnC" in i[0])])].groupby("Senescent_Cell_Label_by_Type").apply(
# lambda x: x.head(3)))
for x in self.rna.obs[col_celltype].unique():
df_snc_markers[x] = self.find_markers(
col_celltype="Senescent_Cell_Label_by_Type",
reference=x, groups=[x, f"SnC {x}"], rankby_abs=True,
inplace=False).reset_index(0, drop=True).assign(**{
col_celltype: f"SnC {x}"}).reset_index().set_index([
col_celltype, "names"])
df_snc_markers = pd.concat(df_snc_markers, names=["Comparison"])
df_snc_markers = df_snc_markers.rename_axis([
"Comparison", col_celltype, "names"]).rename({
"logfoldchanges": "log_fc"}, axis=1).assign(
log_fc_abs=df_snc_markers["logfoldchanges"].abs())
df_snc_markers_top = df_snc_markers[df_snc_markers.pvals_adj < p_threshold]
df_snc_markers_top = df_snc_markers_top[
df_snc_markers.log_fc_abs >= lfc_threshold]
df_snc_markers_top = df_snc_markers_top.groupby([
"Comparison", col_celltype]).apply(
lambda x: x.assign(scores_abs=x.scores.abs()).sort_values(
"scores_abs", ascending=False).head(20),
include_groups=False).reset_index([0, 1], drop=True)
... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_hierarchy_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'SnC_hierarchy_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'SnC_hierarchy' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo_3' as categorical
By Group¶
In [ ]:
res_rank_genes, p_threshold = {}, 1e-10
for x in self.rna.obs[col_batch].unique():
res_rank_genes[x] = {}
for c in self.rna.obs[self.rna.obs[col_batch] == x][
col_celltype].unique():
s_iter = (self.rna.obs[col_batch] == x) & (self.rna.obs[
"Senescent_Cell_Label_by_Type"].isin([c, f"SnC {c}"]))
tmp = sc.tl.rank_genes_groups(
self.rna[s_iter], "Senescent_Cell_Label_by_Type", rankby_abs=True,
layer="log1p", groups=[c, f"SnC {c}"], reference=c, copy=True)
res_rank_genes[x][c] = sc.get.rank_genes_groups_df(
tmp, f"SnC {c}", pval_cutoff=p_threshold)
tmp = sc.tl.rank_genes_groups(
self.rna[self.rna.obs[col_batch] == x],
"Senescent_Cell_Label", rankby_abs=True,
layer="log1p", reference="Non-Senescent", copy=True)
res_rank_genes[x]["Overall"] = sc.get.rank_genes_groups_df(
tmp, f"Senescent", pval_cutoff=p_threshold)
res_rank_genes[x] = pd.concat(res_rank_genes[x], names=[col_celltype])
res_rank_genes = pd.concat(res_rank_genes, names=[col_batch])
res_rank_genes = res_rank_genes.assign(
logfoldchanges_abs=res_rank_genes.logfoldchanges.abs())
res_rank_genes_top = res_rank_genes.reset_index().groupby([
col_batch, col_celltype]).apply(
lambda x: x.sort_values("logfoldchanges_abs").iloc[:20],
include_groups=False).reset_index(2, drop=True).set_index(
"names", append=True).rename_axis([
col_batch, col_celltype, "variable"])
res_rank_genes_top
Plot¶
Comparison¶
In [ ]:
scores = self.rna.obs[[col_batch] + [
f"score_{x}" for x in sen_metrics]].set_index(
col_batch, append=True).reset_index()
sns.pairplot(scores, diag_kind="kde", diag_kws=dict(
cut=0, fill=True, common_norm=True),
hue=col_batch, palette=palette[col_batch])
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x7c539e317cb0>
Scores¶
In [13]:
# UMAP with Senescence Scores
bnds = self.rna.obs["senscore"].describe(percentiles=[0.75, 0.99])
_ = self.plot(kind="umap", color=["senscore", col_celltype], umap=dict(
palette=None, color_map="Reds", vmin=bnds["75%"], vmax=bnds["99%"]))
# Violin Plot of Senescence Scores
_ = self.plot(kind="violin", genes=["senscore"], common_norm=True,
col_celltype=col_celltype, rotation=90)
_ = self.plot(kind="violin", genes=["senscore"], common_norm=True,
by_group=col_celltype, col_wrap=1,
col_celltype=col_batch, rotation=90)
# KDEs
fig_sen_kde = {}
for x in [col_age, col_condition, col_batch]:
fig_sen_kde[x] = sns.displot(self.rna.obs, hue=x, x="senscore",
palette=palette[x], kind="kde",
cut=0, common_norm=True, fill=True)
# Overall (All Cell Types)
fig_sen_overall = sns.catplot(self.rna.obs, x=col_condition,
hue=col_age, y="senscore",
palette=palette[col_age],
kind="violin")
fig_sen_overall_2 = sns.catplot(self.rna.obs, x=col_age, hue=col_condition,
palette=palette[col_condition],
y="senscore", kind="violin")
# By Cell Type
fig_sen_rc = sns.catplot(self.rna.obs, col=col_celltype, y="senscore",
palette=palette[col_condition],
hue=col_condition, x=col_age, kind="violin",
height=10, aspect=2, col_wrap=3)
fig_sen = sns.catplot(self.rna.obs, x=col_celltype, y="senscore",
palette=palette[col_batch],
hue=col_batch, kind="box", height=10, aspect=2)
for a in fig_sen.axes.flatten():
a.tick_params(axis="x", labelrotation=90)
fig_sen_2 = sns.catplot(self.rna.obs, x=col_celltype, y="senscore",
col=col_age, hue=col_condition,
palette=palette[col_condition],
kind="box", height=10, aspect=2)
for a in fig_sen_2.axes.flatten():
a.tick_params(axis="x", labelrotation=90)
fig_sen_3 = sns.catplot(self.rna.obs, x=col_celltype, y="senscore",
col=col_condition, hue=col_age,
palette=palette[col_age],
kind="box", height=10, aspect=2)
for a in fig_sen_3.axes.flatten():
a.tick_params(axis="x", labelrotation=90)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
Burden¶
Group by sample and cell type and get percentage of senescent cells
In [14]:
grps = [col_sample, col_celltype, col_age, col_condition, col_batch]
dff = self.rna.obs[grps + ["Senescent_Cell"]].groupby(grps).apply(
lambda x: x["Senescent_Cell"].mean(), include_groups=False
).to_frame("Senescent_Cell_Burden") * 100
# kws_plot = dict(kind="violin", split=False, , common_norm=True)
kws_plot = dict(kind="box", errorbar=("ci", 95))
sns.catplot(y="Senescent_Cell_Burden", x=col_condition, **kws_plot,
palette=palette[col_condition],
hue=col_condition, data=dff, height=3, aspect=1.5)
sns.catplot(y="Senescent_Cell_Burden", x=col_condition, **kws_plot,
col=col_celltype, col_wrap=4,
palette=palette[col_age],
hue=col_age, data=dff, height=3, aspect=1.5)
sns.catplot(y="Senescent_Cell_Burden", hue=col_condition, **kws_plot,
col=col_celltype, col_wrap=4,
palette=palette[col_condition],
x=col_age, data=dff, height=3, aspect=1.5)
sns.catplot(y="Senescent_Cell_Burden", x=col_celltype, **kws_plot,
palette=palette[col_batch],
hue=col_batch, data=dff, height=8, aspect=1.5)
for x in [col_condition, col_age, [col_condition, col_age]]:
print("\n\n", round(dff["Senescent_Cell_Burden"].groupby(x).describe()[[
"min", "25%", "50%", "75%", "max"]], 1))
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
min 25% 50% 75% max
Condition
Ground Control 0.0 0.4 1.0 2.0 6.2
Space Flight 0.0 0.9 1.8 2.3 11.1
min 25% 50% 75% max
Age_End
20 Weeks 0.0 0.5 1.2 2.0 5.0
37 Weeks 0.0 0.5 1.6 3.2 11.1
min 25% 50% 75% max
Condition Age_End
Ground Control 20 Weeks 0.0 0.4 0.8 1.7 5.0
37 Weeks 0.0 0.4 1.5 3.2 6.2
Space Flight 20 Weeks 0.0 0.8 1.6 2.1 3.3
37 Weeks 0.0 0.9 1.9 2.9 11.1
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
Analyze¶
Regressions & ANOVAs & MEMs¶
In [15]:
r_dff = polars.DataFrame(dff.reset_index()) # pymer-compatible aggregated df
r_rna = polars.DataFrame(self.rna.obs.rename({
col_celltype: "CT_"}, axis=1).copy()) # pymer-compatible cell-level df
lvls = ["key_control", "key_treatment"]
factors = {col_condition: [keys[col_condition][i] for i in lvls],
col_age: [keys[col_age][i] for i in lvls]}
Scores¶
Cell Type Random Effects¶
In [ ]:
# # Age + Condition
# model_ca_mlm = lmer(f"senscore ~ {col_condition} + {col_age} +"
# f"(1 | {col_sample})", data=r_rna) # set up model
# model_ca_mlm.set_factors(factors)
# model_ca_mlm.set_transforms({"senscore": "zscore"})
# model_ca_mlm.anova(summary=True).show()
# # Age * Condition
# model_ca_mlm_i = lmer(
# f"senscore ~ {col_condition} * {col_age} + "
# f"(1 | {col_sample})", data=r_rna) # set up model
# model_ca_mlm_i.set_factors(factors)
# model_ca_mlm_i.set_transforms({"senscore": "zscore"})
# model_ca_mlm_i.anova(summary=True).show()
# # Age * Condition (Condition as Random Effect)
# model_ca_mlm_ii = lmer(
# f"senscore ~ {col_condition} * {col_age} + "
# f"(1 | {col_sample}) + (1 + {col_condition} | CT_)",
# data=r_rna) # set up model
# model_ca_mlm_ii.set_factors(factors)
# model_ca_mlm_ii.set_transforms({"senscore": "zscore"})
# model_ca_mlm_ii.anova(summary=True).show()
# Age * Condition (Age and Condition as Random Effect)
model_ca_mlm_iii = lmer(
f"senscore ~ {col_condition} * {col_age} + (1 | {col_sample})"
f" + (1 + {col_condition} + {col_age} | CT_)",
data=r_rna) # set up model
model_ca_mlm_iii.set_factors(factors)
model_ca_mlm_iii.set_transforms({"senscore": "zscore"})
model_ca_mlm_iii.anova(summary=True).show()
# Model Comparison
# compare(model_ca_mlm, model_ca_mlm_i, model_ca_mlm_i,
# model_ca_mlm_ii, model_ca_mlm_iii).show()
# GLM Version of Results
# model_ca_mlm.summary().show()
# model_ca_mlm_i.summary().show()
# model_ca_mlm_ii.summary().show()
model_ca_mlm_iii.summary().show()
R messages: Note: D.f. calculations have been disabled because the number of observations exceeds 3000. To enable adjustments, add the argument 'lmerTest.limit = 103274' (or larger) [or, globally, 'set emm_options(lmerTest.limit = 103274)' or larger]; but be warned that this may result in large computation time and memory use. R messages: Note: D.f. calculations have been disabled because the number of observations exceeds 3000. To enable adjustments, add the argument 'lmerTest.limit = 103274' (or larger) [or, globally, 'set emm_options(lmerTest.limit = 103274)' or larger]; but be warned that this may result in large computation time and memory use.
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Condition | 1.000 | inf | 5.960 | 5.960 | 0.01463 | * |
| Age_End | 1.000 | inf | 2.150 | 2.150 | 0.1426 | |
| Condition:Age_End | 1.000 | inf | 0.349 | 0.349 | 0.5549 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Formula: lmer(senscore~Condition*Age_End+(1|sample)+(1+Condition+Age_End|CT_)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -137950 AIC: 275925 | BIC: 276040 Residual error: 0.919 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.159 | |||||||
| CT_-sd | (Intercept) | 0.636 | |||||||
| CT_-sd | ConditionSpace Flight | 0.148 | |||||||
| CT_-sd | Age_End37 Weeks | 0.256 | |||||||
| CT_-cor | (Intercept) | 0.042 | |||||||
| CT_-cor | (Intercept) | 0.564 | |||||||
| CT_-cor | ConditionSpace Flight | −0.793 | |||||||
| Residual-sd | Observation | 0.919 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.330 | 0.269 | −0.336 | 0.995 | 1.224 | 5.769 | 0.2687 | ||
| ConditionSpace Flight | 0.283 | 0.123 | 0.023 | 0.542 | 2.290 | 17.931 | 0.03436 | * | |
| Age_End37 Weeks | 0.235 | 0.150 | −0.087 | 0.557 | 1.565 | 14.053 | 0.1399 | ||
| ConditionSpace Flight:Age_End37 Weeks | −0.089 | 0.152 | −0.415 | 0.236 | −0.590 | 13.918 | 0.5644 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Cell Type as Moderator¶
In [32]:
# Age + Condition * Cell Type
model_cac_mlm_i2 = lmer(
f"senscore ~ Aged + Spaceflight * CT_ + "
f"(1 | {col_sample})", data=r_rna) # set up model
model_cac_mlm_i2.set_transforms({"senscore": "zscore"})
model_cac_mlm_i2.anova(summary=True).show()
# Age * Condition * Cell Type
model_cac_mlm_i2b = lmer(
f"senscore ~ Aged * Spaceflight + Spaceflight * CT_ + "
f"(1 | {col_sample})", data=r_rna) # set up model
model_cac_mlm_i2b.set_transforms({"senscore": "zscore"})
model_cac_mlm_i2b.anova(summary=True).show()
# Age * Condition * Cell Type
model_cac_mlm_i3 = lmer(
f"senscore ~ Aged * Spaceflight * CT_ + "
f"(1 | {col_sample})", data=r_rna) # set up model
model_cac_mlm_i3.set_transforms({"senscore": "zscore"})
model_cac_mlm_i3.anova(summary=True).show()
# GLM Version of Results
model_cac_mlm_i2.summary().show()
model_cac_mlm_i2b.summary().show()
model_cac_mlm_i3.summary().show()
# Model Comparison
compare(model_cac_mlm_i2, model_cac_mlm_i2b, model_cac_mlm_i3).show()
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Aged | 1.000 | inf | 5.640 | 5.640 | 0.01755 | * |
| Spaceflight | 1.000 | inf | 11.454 | 11.454 | <.001 | *** |
| annotation_scanvi_collapsed | 5.000 | inf | 2,746.320 | 13,731.600 | *** | |
| Spaceflight:annotation_scanvi_collapsed | 5.000 | inf | 29.008 | 145.040 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Aged | 1.000 | inf | 5.415 | 5.415 | 0.01996 | * |
| Spaceflight | 1.000 | inf | 11.021 | 11.021 | <.001 | *** |
| annotation_scanvi_collapsed | 5.000 | inf | 2,746.334 | 13,731.670 | *** | |
| Aged:Spaceflight | 1.000 | inf | 0.408 | 0.408 | 0.5228 | |
| Spaceflight:annotation_scanvi_collapsed | 5.000 | inf | 28.996 | 144.980 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Aged | 1.000 | inf | 5.981 | 5.981 | 0.01446 | * |
| Spaceflight | 1.000 | inf | 9.339 | 9.339 | 0.002243 | ** |
| annotation_scanvi_collapsed | 5.000 | inf | 2,669.438 | 13,347.190 | *** | |
| Aged:Spaceflight | 1.000 | inf | 0.130 | 0.130 | 0.7185 | |
| Aged:annotation_scanvi_collapsed | 5.000 | inf | 37.304 | 186.520 | <.001 | *** |
| Spaceflight:annotation_scanvi_collapsed | 5.000 | inf | 34.950 | 174.750 | <.001 | *** |
| Aged:Spaceflight:annotation_scanvi_collapsed | 5.000 | inf | 18.125 | 90.625 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Formula: lmer(senscore~Aged+Spaceflight*annotation_scanvi_collapsed+(1|sample)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -138067 AIC: 276164 | BIC: 276307 Residual error: 0.921 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.157 | |||||||
| Residual-sd | Observation | 0.921 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.439 | 0.064 | 0.304 | 0.574 | 6.898 | 16.211 | <.001 | *** | |
| Aged | 0.178 | 0.075 | 0.018 | 0.338 | 2.375 | 14.933 | 0.03139 | * | |
| Spaceflight | 0.309 | 0.078 | 0.145 | 0.473 | 3.954 | 17.619 | <.001 | *** | |
| annotation_scanvi_collapsedEndothelial | 0.886 | 0.038 | 0.812 | 0.960 | 23.426 | 103,248.906 | <.001 | *** | |
| annotation_scanvi_collapsedMicroglial | 0.450 | 0.025 | 0.400 | 0.499 | 17.750 | 103,246.539 | <.001 | *** | |
| annotation_scanvi_collapsedNeuron | −0.675 | 0.014 | −0.703 | −0.647 | −47.191 | 103,253.259 | *** | ||
| annotation_scanvi_collapsedOPC | −0.292 | 0.024 | −0.338 | −0.245 | −12.372 | 103,249.458 | <.001 | *** | |
| annotation_scanvi_collapsedOligodendrocyte | −0.921 | 0.016 | −0.952 | −0.889 | −56.980 | 103,248.648 | *** | ||
| Spaceflight:annotation_scanvi_collapsedEndothelial | −0.105 | 0.079 | −0.260 | 0.050 | −1.326 | 103,251.597 | 0.185 | ||
| Spaceflight:annotation_scanvi_collapsedMicroglial | 0.054 | 0.039 | −0.023 | 0.131 | 1.382 | 103,246.538 | 0.1669 | ||
| Spaceflight:annotation_scanvi_collapsedNeuron | −0.177 | 0.024 | −0.224 | −0.130 | −7.342 | 103,255.169 | <.001 | *** | |
| Spaceflight:annotation_scanvi_collapsedOPC | 0.078 | 0.039 | 0.000 | 0.155 | 1.971 | 103,249.550 | 0.04874 | * | |
| Spaceflight:annotation_scanvi_collapsedOligodendrocyte | −0.154 | 0.027 | −0.206 | −0.102 | −5.786 | 103,248.885 | <.001 | *** | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Formula: lmer(senscore~Aged*Spaceflight+Spaceflight*annotation_scanvi_collapsed+(1|sample)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -138068 AIC: 276168 | BIC: 276320 Residual error: 0.921 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.161 | |||||||
| Residual-sd | Observation | 0.921 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.417 | 0.073 | 0.261 | 0.573 | 5.700 | 14.781 | <.001 | *** | |
| Aged | 0.227 | 0.108 | −0.005 | 0.459 | 2.099 | 13.868 | 0.05458 | . | |
| Spaceflight | 0.358 | 0.110 | 0.123 | 0.593 | 3.243 | 15.072 | 0.00543 | ** | |
| annotation_scanvi_collapsedEndothelial | 0.886 | 0.038 | 0.812 | 0.960 | 23.427 | 103,248.573 | <.001 | *** | |
| annotation_scanvi_collapsedMicroglial | 0.450 | 0.025 | 0.400 | 0.499 | 17.750 | 103,246.502 | <.001 | *** | |
| annotation_scanvi_collapsedNeuron | −0.675 | 0.014 | −0.703 | −0.647 | −47.192 | 103,252.374 | *** | ||
| annotation_scanvi_collapsedOPC | −0.292 | 0.024 | −0.338 | −0.245 | −12.372 | 103,249.147 | <.001 | *** | |
| annotation_scanvi_collapsedOligodendrocyte | −0.921 | 0.016 | −0.952 | −0.889 | −56.979 | 103,248.519 | *** | ||
| Aged:Spaceflight | −0.098 | 0.153 | −0.426 | 0.230 | −0.639 | 13.920 | 0.5331 | ||
| Spaceflight:annotation_scanvi_collapsedEndothelial | −0.105 | 0.079 | −0.260 | 0.050 | −1.325 | 103,251.272 | 0.1851 | ||
| Spaceflight:annotation_scanvi_collapsedMicroglial | 0.054 | 0.039 | −0.023 | 0.131 | 1.382 | 103,246.493 | 0.167 | ||
| Spaceflight:annotation_scanvi_collapsedNeuron | −0.177 | 0.024 | −0.224 | −0.130 | −7.340 | 103,253.333 | <.001 | *** | |
| Spaceflight:annotation_scanvi_collapsedOPC | 0.078 | 0.039 | 0.000 | 0.155 | 1.971 | 103,249.395 | 0.04876 | * | |
| Spaceflight:annotation_scanvi_collapsedOligodendrocyte | −0.154 | 0.027 | −0.206 | −0.102 | −5.787 | 103,248.734 | <.001 | *** | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Formula: lmer(senscore~Aged*Spaceflight*annotation_scanvi_collapsed+(1|sample)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -137917 AIC: 275887 | BIC: 276135 Residual error: 0.919 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.160 | |||||||
| Residual-sd | Observation | 0.919 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.501 | 0.074 | 0.345 | 0.658 | 6.807 | 15.524 | <.001 | *** | |
| Aged | 0.037 | 0.111 | −0.198 | 0.271 | 0.332 | 15.559 | 0.7445 | ||
| Spaceflight | 0.183 | 0.112 | −0.054 | 0.419 | 1.636 | 16.243 | 0.1211 | ||
| annotation_scanvi_collapsedEndothelial | 0.651 | 0.045 | 0.564 | 0.739 | 14.573 | 103,239.106 | <.001 | *** | |
| annotation_scanvi_collapsedMicroglial | 0.385 | 0.034 | 0.320 | 0.451 | 11.492 | 103,236.384 | <.001 | *** | |
| annotation_scanvi_collapsedNeuron | −0.794 | 0.019 | −0.831 | −0.756 | −41.346 | 103,237.111 | *** | ||
| annotation_scanvi_collapsedOPC | −0.248 | 0.030 | −0.307 | −0.189 | −8.212 | 103,240.534 | <.001 | *** | |
| annotation_scanvi_collapsedOligodendrocyte | −0.957 | 0.021 | −0.999 | −0.915 | −44.915 | 103,237.945 | *** | ||
| Aged:Spaceflight | 0.270 | 0.158 | −0.064 | 0.605 | 1.709 | 16.359 | 0.1064 | ||
| Aged:annotation_scanvi_collapsedEndothelial | 0.768 | 0.085 | 0.602 | 0.935 | 9.035 | 103,239.001 | <.001 | *** | |
| Aged:annotation_scanvi_collapsedMicroglial | 0.146 | 0.051 | 0.046 | 0.246 | 2.859 | 103,236.545 | 0.004246 | ** | |
| Aged:annotation_scanvi_collapsedNeuron | 0.257 | 0.029 | 0.200 | 0.313 | 8.925 | 103,242.983 | <.001 | *** | |
| Aged:annotation_scanvi_collapsedOPC | −0.158 | 0.048 | −0.253 | −0.063 | −3.270 | 103,238.157 | 0.001078 | ** | |
| Aged:annotation_scanvi_collapsedOligodendrocyte | 0.073 | 0.033 | 0.009 | 0.137 | 2.235 | 103,238.959 | 0.02542 | * | |
| Spaceflight:annotation_scanvi_collapsedEndothelial | 0.037 | 0.135 | −0.229 | 0.302 | 0.272 | 103,238.411 | 0.7855 | ||
| Spaceflight:annotation_scanvi_collapsedMicroglial | 0.239 | 0.054 | 0.133 | 0.345 | 4.419 | 103,236.398 | <.001 | *** | |
| Spaceflight:annotation_scanvi_collapsedNeuron | 0.016 | 0.034 | −0.051 | 0.082 | 0.463 | 103,237.130 | 0.6436 | ||
| Spaceflight:annotation_scanvi_collapsedOPC | 0.167 | 0.054 | 0.061 | 0.273 | 3.098 | 103,237.478 | 0.001951 | ** | |
| Spaceflight:annotation_scanvi_collapsedOligodendrocyte | 0.035 | 0.037 | −0.037 | 0.107 | 0.946 | 103,239.194 | 0.3443 | ||
| Aged:Spaceflight:annotation_scanvi_collapsedEndothelial | −0.690 | 0.175 | −1.032 | −0.347 | −3.946 | 103,239.781 | <.001 | *** | |
| Aged:Spaceflight:annotation_scanvi_collapsedMicroglial | −0.383 | 0.079 | −0.537 | −0.229 | −4.868 | 103,236.505 | <.001 | *** | |
| Aged:Spaceflight:annotation_scanvi_collapsedNeuron | −0.406 | 0.048 | −0.500 | −0.311 | −8.407 | 103,243.233 | <.001 | *** | |
| Aged:Spaceflight:annotation_scanvi_collapsedOPC | −0.105 | 0.079 | −0.260 | 0.051 | −1.319 | 103,239.000 | 0.1871 | ||
| Aged:Spaceflight:annotation_scanvi_collapsedOligodendrocyte | −0.375 | 0.053 | −0.480 | −0.270 | −7.022 | 103,238.919 | <.001 | *** | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Analysis of Deviance Table | |||||||||
| Model 1: lmer(senscore~Aged+Spaceflightannotation_scanvi_collapsed+(1|sample)) Model 2: lmer(senscore~AgedSpaceflight+Spaceflightannotation_scanvi_collapsed+(1|sample)) Model 3: lmer(senscore~AgedSpaceflight*annotation_scanvi_collapsed+(1|sample)) |
|||||||||
| AIC | BIC | logLik | npar | -2*log(L) | Chisq | Df | Pr(>Chisq) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 276164.7 | 276307.9 | −138067.4 | 15.00 | 276.06K | ||||
| 2.00 | 276168.3 | 276321 | −138068.1 | 16.00 | 276.06K | 0.52 | 1.00 | 0.47 | |
| 3.00 | 275887.1 | 276135.3 | −137917.5 | 26.00 | 275.72K | 345.22 | 10.00 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Binary Snc/Not¶
Multilevel Logistic Regression
Cell Type Random Effects¶
In [38]:
glmmod = {}
# for x in ["1", f"1 + {col_condition}", f"1 + {col_condition} + {col_age}"]:
# fff = str(f"Senescent_Cell ~ {col_age} * {col_condition}"
# f" + (1 | {col_sample}) + ({x} | CT_)")
for x in ["1", f"1 + Spaceflight", f"1 + Spaceflight + Aged"]:
sep = "|" if x == "1" else "||"
fff = str(f"Senescent_Cell ~ Aged * Spaceflight"
f" + (1 | {col_sample}) + ({x} {sep} CT_)")
glmmod[x] = glmer(fff, data=r_rna, family="binomial")
# glmmod[x].set_factors(factors)
glmmod[x].fit(exponentiate=True, ncpus=ncpus) # estimates => odds
# print(glmmod[x].emmeans(col_condition))
# print(glmmod[x].emmeans(col_age))
glmmod[x].summary().show()
# print(glmmod[x].ranef["CT_"])
compare(*[glmmod[x] for x in glmmod])
# Plot
# grid = sns.FacetGrid(self.rna.obs, col=col_age, hue=col_condition)
# grid.map(sns.countplot, "Senescent_Cell", order=[False, True])
# grid.add_legend()
# grid.set_xlabels("")
# grid.set_xticklabels(["Not SnC", "Snc"])
# sns.despine()
# fig = plt.figure(figsize=(10, 2))
# g_s = fig.add_gridspec(1, 2, width_ratios=[1, 3])
# left, right = fig.add_subplot(g_s[0]), fig.add_subplot(g_s[1])
# left = sns.kdeplot(x="resid", data=glmmod.data, ax=left)
# left.set(xlabel="Residuals", title="Error Distribution")
# right.stem(glmmod.data["cooksd"], basefmt=" ", label="Cook's Distance")
# right.set(xlabel="Row Number", ylabel="Cook's Distance",
# title="Influential Observations",
# xticks=range(glmmod.data.height))
# sns.despine()
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1|CT_)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -9686 AIC: 19384 | BIC: 19441 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.567 | |||||||
| CT_-sd | (Intercept) | 0.159 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.004 | 0.015 | 0.003 | −14.011 | inf | <.001 | *** | |
| Aged | 2.015 | 0.688 | 5.895 | 1.104 | 1.279 | inf | 0.201 | ||
| Spaceflight | 2.211 | 0.818 | 5.976 | 1.122 | 1.564 | inf | 0.1177 | ||
| Aged:Spaceflight | 0.839 | 0.179 | 3.925 | 0.660 | −0.223 | inf | 0.8232 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1+Spaceflight||CT_)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -9686 AIC: 19386 | BIC: 19453 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.567 | |||||||
| CT_-sd | (Intercept) | 0.159 | |||||||
| CT_.1-sd | Spaceflight | 0.000 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.012 | 0.002 | −19.452 | inf | <.001 | *** | |
| Aged | 2.015 | 1.085 | 3.742 | 0.636 | 2.218 | inf | 0.02658 | * | |
| Spaceflight | 2.211 | 1.116 | 4.381 | 0.771 | 2.275 | inf | 0.02291 | * | |
| Aged:Spaceflight | 0.839 | 0.371 | 1.898 | 0.349 | −0.422 | inf | 0.6728 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1+Spaceflight+Aged||CT_)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -9675 AIC: 19367 | BIC: 19443 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.565 | |||||||
| CT_-sd | (Intercept) | 0.000 | |||||||
| CT_.1-sd | Spaceflight | 0.000 | |||||||
| CT_.2-sd | Aged | 0.275 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.013 | 0.002 | −20.199 | inf | <.001 | *** | |
| Aged | 1.787 | 0.869 | 3.675 | 0.657 | 1.578 | inf | 0.1145 | ||
| Spaceflight | 2.196 | 1.117 | 4.319 | 0.758 | 2.281 | inf | 0.02256 | * | |
| Aged:Spaceflight | 0.848 | 0.343 | 2.099 | 0.392 | −0.357 | inf | 0.7215 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Out[38]:
| Analysis of Deviance Table | |||||||||
| Model 1: glmer(Senescent_Cell~AgedSpaceflight+(1|sample)+(1|CT_)) Model 2: glmer(Senescent_Cell~AgedSpaceflight+(1|sample)+(1+Spaceflight||CT_)) Model 3: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1+Spaceflight+Aged||CT_)) |
|||||||||
| AIC | BIC | logLik | npar | -2*log(L) | Chisq | Df | Pr(>Chisq) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 19384.2 | 19441.5 | −9686.1 | 6.00 | 19.37K | ||||
| 2.00 | 19386.2 | 19453 | −9686.1 | 7.00 | 19.37K | 1.00 | 1.0 | ||
| 3.00 | 19367.6 | 19443.9 | −9675.8 | 8.00 | 19.35K | 20.62 | 1.00 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Cell Type as Moderator¶
In [39]:
# 2-Way Interaction
fff = str(f"Senescent_Cell ~ Aged + Spaceflight * CT_ + "
"(1 | sample)")
glmmod_ct2 = glmer(fff, data=r_rna, family="binomial")
glmmod_ct2.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
glmmod_ct2.summary().show()
# 2 2-Way Interactions
fff = str(f"Senescent_Cell ~ Aged * Spaceflight + Spaceflight * CT_"
f" + (1 | {col_sample})")
glmmod_ct2b = glmer(fff, data=r_rna, family="binomial")
glmmod_ct2b.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
glmmod_ct2b.anova(summary=True).show()
glmmod_ct2b.summary().show()
# 3-Way Interaction
fff = str(f"Senescent_Cell ~ Aged * Spaceflight * CT_"
f" + (1 | {col_sample})")
model_allix = glmer(fff, data=r_rna, family="binomial")
model_allix.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
model_allix.anova(summary=True).show()
model_allix.summary().show()
res_allix_fixed = pd.DataFrame(model_allix.result_fit[[
"term", "estimate", "p_value"]], columns=[
"Term", "OR", "P"]).set_index("Term")
res_allix_fixed.loc[:, "Log-Odds"] = np.log(
res_allix_fixed.OR.astype(float))
res_allix_fixed.loc[:, "Significance"] = res_allix_fixed.P.apply(
lambda x: "***" if x < 0.001 else "**" if x < 0.01 else "*" if (
x < 0.05) else "")
print(res_allix_fixed)
# Model Comparison
compare(glmmod_ct2, glmmod_ct2b, model_allix)
| Formula: glmer(Senescent_Cell~Aged+Spaceflight*CT_+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -9678 AIC: 19385 | BIC: 19518 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.564 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.012 | 0.002 | −19.911 | inf | <.001 | *** | |
| Aged | 1.844 | 1.107 | 3.070 | 0.480 | 2.352 | inf | 0.01867 | * | |
| Spaceflight | 2.545 | 1.372 | 4.719 | 0.802 | 2.964 | inf | 0.003037 | ** | |
| CT_Endothelial | 1.474 | 0.802 | 2.708 | 0.457 | 1.250 | inf | 0.2113 | ||
| CT_Microglial | 1.245 | 0.811 | 1.911 | 0.272 | 1.004 | inf | 0.3155 | ||
| CT_Neuron | 1.219 | 0.953 | 1.558 | 0.153 | 1.579 | inf | 0.1142 | ||
| CT_OPC | 0.773 | 0.497 | 1.202 | 0.174 | −1.143 | inf | 0.253 | ||
| CT_Oligodendrocyte | 0.820 | 0.610 | 1.104 | 0.124 | −1.308 | inf | 0.1907 | ||
| Spaceflight:CT_Endothelial | 0.359 | 0.091 | 1.416 | 0.251 | −1.463 | inf | 0.1434 | ||
| Spaceflight:CT_Microglial | 0.706 | 0.384 | 1.299 | 0.220 | −1.118 | inf | 0.2637 | ||
| Spaceflight:CT_Neuron | 0.781 | 0.548 | 1.114 | 0.141 | −1.364 | inf | 0.1725 | ||
| Spaceflight:CT_OPC | 0.878 | 0.466 | 1.655 | 0.284 | −0.402 | inf | 0.6879 | ||
| Spaceflight:CT_Oligodendrocyte | 0.813 | 0.535 | 1.235 | 0.174 | −0.971 | inf | 0.3316 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Aged | 1.000 | inf | 5.378 | 5.378 | 0.02039 | * |
| Spaceflight | 1.000 | inf | 4.301 | 4.301 | 0.03808 | * |
| CT_ | 5.000 | inf | 6.731 | 33.655 | <.001 | *** |
| Aged:Spaceflight | 1.000 | inf | 0.110 | 0.110 | 0.7399 | |
| Spaceflight:CT_ | 5.000 | inf | 0.862 | 4.310 | 0.5054 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+Spaceflight*CT_+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -9678 AIC: 19387 | BIC: 19530 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.563 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.007 | 0.004 | 0.012 | 0.002 | −18.198 | inf | <.001 | *** | |
| Aged | 2.010 | 0.995 | 4.060 | 0.721 | 1.947 | inf | 0.05157 | . | |
| Spaceflight | 2.773 | 1.247 | 6.169 | 1.131 | 2.500 | inf | 0.01241 | * | |
| CT_Endothelial | 1.474 | 0.849 | 2.561 | 0.415 | 1.378 | inf | 0.1682 | ||
| CT_Microglial | 1.245 | 0.832 | 1.862 | 0.256 | 1.066 | inf | 0.2863 | ||
| CT_Neuron | 1.219 | 0.960 | 1.546 | 0.148 | 1.628 | inf | 0.1036 | ||
| CT_OPC | 0.773 | 0.505 | 1.182 | 0.168 | −1.187 | inf | 0.2351 | ||
| CT_Oligodendrocyte | 0.820 | 0.615 | 1.095 | 0.121 | −1.344 | inf | 0.1788 | ||
| Aged:Spaceflight | 0.843 | 0.307 | 2.314 | 0.434 | −0.332 | inf | 0.7399 | ||
| Spaceflight:CT_Endothelial | 0.359 | 0.108 | 1.195 | 0.220 | −1.669 | inf | 0.09505 | . | |
| Spaceflight:CT_Microglial | 0.707 | 0.399 | 1.252 | 0.206 | −1.190 | inf | 0.234 | ||
| Spaceflight:CT_Neuron | 0.782 | 0.553 | 1.104 | 0.138 | −1.400 | inf | 0.1616 | ||
| Spaceflight:CT_OPC | 0.878 | 0.475 | 1.624 | 0.275 | −0.414 | inf | 0.6788 | ||
| Spaceflight:CT_Oligodendrocyte | 0.813 | 0.539 | 1.226 | 0.170 | −0.989 | inf | 0.3225 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Aged | 1.000 | inf | 0.018 | 0.018 | 0.8941 | |
| Spaceflight | 1.000 | inf | 0.000 | 0.000 | 0.9899 | |
| CT_ | 5.000 | inf | 5.372 | 26.860 | <.001 | *** |
| Aged:Spaceflight | 1.000 | inf | 0.005 | 0.005 | 0.9463 | |
| Aged:CT_ | 5.000 | inf | 4.709 | 23.545 | <.001 | *** |
| Spaceflight:CT_ | 5.000 | inf | 0.225 | 1.125 | 0.9517 | |
| Aged:Spaceflight:CT_ | 5.000 | inf | 1.298 | 6.490 | 0.2616 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Formula: glmer(Senescent_Cell~Aged*Spaceflight*CT_+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -9661 AIC: 19373 | BIC: 19612 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.564 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.016 | 0.003 | −14.872 | inf | <.001 | *** | |
| Aged | 1.558 | 0.627 | 3.867 | 0.723 | 0.955 | inf | 0.3394 | ||
| Spaceflight | 2.097 | 0.821 | 5.356 | 1.003 | 1.548 | inf | 0.1217 | ||
| CT_Endothelial | 0.886 | 0.363 | 2.164 | 0.404 | −0.266 | inf | 0.79 | ||
| CT_Microglial | 0.959 | 0.473 | 1.944 | 0.346 | −0.116 | inf | 0.9077 | ||
| CT_Neuron | 0.922 | 0.615 | 1.384 | 0.191 | −0.391 | inf | 0.696 | ||
| CT_OPC | 0.808 | 0.427 | 1.528 | 0.263 | −0.657 | inf | 0.5111 | ||
| CT_Oligodendrocyte | 1.052 | 0.670 | 1.653 | 0.242 | 0.221 | inf | 0.8254 | ||
| Aged:Spaceflight | 1.346 | 0.372 | 4.876 | 0.884 | 0.453 | inf | 0.6507 | ||
| Aged:CT_Endothelial | 3.136 | 0.887 | 11.084 | 2.020 | 1.774 | inf | 0.07607 | . | |
| Aged:CT_Microglial | 1.561 | 0.624 | 3.908 | 0.731 | 0.952 | inf | 0.3413 | ||
| Aged:CT_Neuron | 1.553 | 0.903 | 2.671 | 0.430 | 1.591 | inf | 0.1116 | ||
| Aged:CT_OPC | 0.821 | 0.308 | 2.189 | 0.411 | −0.394 | inf | 0.6936 | ||
| Aged:CT_Oligodendrocyte | 0.581 | 0.305 | 1.106 | 0.191 | −1.653 | inf | 0.09825 | . | |
| Spaceflight:CT_Endothelial | 0.000 | 0.000 | 111,405,869,631,547,604,087,818,516,773,174,609,728,473,284,365,593,369,837,211,609,575,012,794,270,753,167,976,268,750,705,917,952.000 | 0.010 | −0.078 | inf | 0.9376 | ||
| Spaceflight:CT_Microglial | 1.219 | 0.466 | 3.185 | 0.597 | 0.403 | inf | 0.6867 | ||
| Spaceflight:CT_Neuron | 1.119 | 0.612 | 2.046 | 0.345 | 0.364 | inf | 0.7159 | ||
| Spaceflight:CT_OPC | 0.971 | 0.354 | 2.664 | 0.500 | −0.056 | inf | 0.955 | ||
| Spaceflight:CT_Oligodendrocyte | 0.871 | 0.448 | 1.696 | 0.296 | −0.406 | inf | 0.685 | ||
| Aged:Spaceflight:CT_Endothelial | 2,592.647 | 0.000 | 3,470,525,015,620,308,636,691,787,938,331,072,880,123,331,428,842,502,710,563,748,259,550,357,545,493,638,604,971,004,467,844,966,489,522,176.000 | 311,064.284 | 0.066 | inf | 0.9478 | ||
| Aged:Spaceflight:CT_Microglial | 0.394 | 0.112 | 1.389 | 0.253 | −1.448 | inf | 0.1476 | ||
| Aged:Spaceflight:CT_Neuron | 0.561 | 0.258 | 1.221 | 0.222 | −1.457 | inf | 0.1451 | ||
| Aged:Spaceflight:CT_OPC | 0.965 | 0.240 | 3.886 | 0.686 | −0.050 | inf | 0.9604 | ||
| Aged:Spaceflight:CT_Oligodendrocyte | 0.985 | 0.399 | 2.431 | 0.454 | −0.033 | inf | 0.9737 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
OR P Log-Odds Significance Term (Intercept) 0.008471 0.0 -4.771158 *** Aged 1.557754 0.339353 0.443245 Spaceflight 2.096864 0.12174 0.740443 CT_Endothelial 0.885702 0.790006 -0.121375 CT_Microglial 0.959074 0.907739 -0.041787 CT_Neuron 0.922298 0.696048 -0.080886 CT_OPC 0.807503 0.511099 -0.213809 CT_Oligodendrocyte 1.052152 0.825401 0.050837 Aged:Spaceflight 1.346216 0.650713 0.297298 Aged:CT_Endothelial 3.135627 0.076072 1.142829 Aged:CT_Microglial 1.561155 0.341331 0.445426 Aged:CT_Neuron 1.552897 0.111627 0.440122 Aged:CT_OPC 0.82106 0.693563 -0.197159 Aged:CT_Oligodendrocyte 0.580681 0.098248 -0.543554 Spaceflight:CT_Endothelial 0.000084 0.937622 -9.389251 Spaceflight:CT_Microglial 1.218564 0.68672 0.197673 Spaceflight:CT_Neuron 1.118644 0.715863 0.112117 Spaceflight:CT_OPC 0.971351 0.954966 -0.029067 Spaceflight:CT_Oligodendrocyte 0.871252 0.685007 -0.137824 Aged:Spaceflight:CT_Endothelial 2592.647409 0.947764 7.860435 Aged:Spaceflight:CT_Microglial 0.39447 0.147619 -0.930213 Aged:Spaceflight:CT_Neuron 0.561191 0.145086 -0.577694 Aged:Spaceflight:CT_OPC 0.965304 0.960361 -0.035313 Aged:Spaceflight:CT_Oligodendrocyte 0.984909 0.973682 -0.015206
Out[39]:
| Analysis of Deviance Table | |||||||||
| Model 1: glmer(Senescent_Cell~Aged+SpaceflightCT_+(1|sample)) Model 2: glmer(Senescent_Cell~AgedSpaceflight+SpaceflightCT_+(1|sample)) Model 3: glmer(Senescent_Cell~AgedSpaceflight*CT_+(1|sample)) |
|||||||||
| AIC | BIC | logLik | npar | -2*log(L) | Chisq | Df | Pr(>Chisq) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 19385.3 | 19518.9 | −9678.6 | 14.00 | 19.36K | ||||
| 2.00 | 19387.2 | 19530.4 | −9678.6 | 15.00 | 19.36K | 0.10 | 1.00 | 0.754 | |
| 3.00 | 19373.8 | 19612.4 | −9661.9 | 25.00 | 19.32K | 33.39 | 10.00 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Burden (Percent SnC)¶
ANOVA: Percent SnCs across (a) Condition x Age and (b) Condition x Age x Cell Type
In [36]:
# Age + Condition
model_1 = lm(
f"Senescent_Cell_Burden ~ {col_condition} + {col_age}", data=r_dff)
model_1.set_factors(factors)
model_1.anova(summary=True).show()
model_1.summary().show()
# Age + Condition + Cell Type
model_2 = lm(
f"Senescent_Cell_Burden ~ {col_condition} + {col_age} + {col_celltype}",
data=r_dff)
model_2.set_factors(factors)
model_2.anova(summary=True).show()
model_2.summary().show()
# Age + Condition * Cell Type
model_3 = lm(
f"Senescent_Cell_Burden ~ {col_age} + {col_condition} * {col_celltype}",
data=r_dff)
model_3.set_factors(factors)
model_3.anova(summary=True).show()
# # Age * Condition * Cell Type
# model_4 = lm(
# f"Senescent_Cell_Burden ~ {col_age} * {col_condition} * {col_celltype}",
# data=r_dff)
# model_4.set_factors(factors)
# model_4.anova(summary=True).show()
# model_4.summary().show()
# Compare
# compare(model_1, model_2, model_3, model_4).show()
for x in [model_1, model_2, model_3]:
x.summary().show()
compare(model_1, model_2, model_3).show()
| ANOVA (Type III tests) | |||||
| model term | df1 | df2 | F_ratio | p_value | |
|---|---|---|---|---|---|
| Condition | 1 | 104 | 1.634 | 0.204 | |
| Age_End | 1 | 104 | 5.018 | 0.02722 | * |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||
| Formula: lm(Senescent_Cell_Burden~Condition+Age_End) | ||||||||
| Number of observations: 107 Confidence intervals: parametric --------------------- R-squared: 0.0657 R-squared-adj: 0.0478 F(2, 104) = 3.659, p = 0.0291 Log-likelihood: -202 AIC: 412 | BIC: 422 Residual error: 1.623 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1.111 | 0.262 | 0.592 | 1.630 | 4.246 | 104 | <.001 | *** |
| ConditionSpace Flight | 0.403 | 0.316 | −0.222 | 1.029 | 1.278 | 104 | 0.204 | |
| Age_End37 Weeks | 0.707 | 0.316 | 0.081 | 1.333 | 2.240 | 104 | 0.02722 | * |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| ANOVA (Type III tests) | |||||
| model term | df1 | df2 | F_ratio | p_value | |
|---|---|---|---|---|---|
| Condition | 1 | 99 | 1.598 | 0.2092 | |
| Age_End | 1 | 99 | 4.947 | 0.0284 | * |
| annotation_scanvi_collapsed | 5 | 99 | 0.943 | 0.4571 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||
| Formula: lm(Senescent_Cell_Burden~Condition+Age_End+annotation_scanvi_collapsed) | ||||||||
| Number of observations: 107 Confidence intervals: parametric --------------------- R-squared: 0.1082 R-squared-adj: 0.0451 F(7, 99) = 1.716, p = 0.1139 Log-likelihood: -199 AIC: 417 | BIC: 441 Residual error: 1.626 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1.230 | 0.438 | 0.362 | 2.099 | 2.810 | 99 | 0.005968 | ** |
| ConditionSpace Flight | 0.400 | 0.316 | −0.228 | 1.027 | 1.264 | 99 | 0.2092 | |
| Age_End37 Weeks | 0.703 | 0.316 | 0.076 | 1.330 | 2.224 | 99 | 0.0284 | * |
| annotation_scanvi_collapsedEndothelial | −0.344 | 0.550 | −1.435 | 0.747 | −0.626 | 99 | 0.5331 | |
| annotation_scanvi_collapsedMicroglial | 0.103 | 0.542 | −0.973 | 1.178 | 0.189 | 99 | 0.8502 | |
| annotation_scanvi_collapsedNeuron | 0.462 | 0.542 | −0.614 | 1.537 | 0.852 | 99 | 0.3963 | |
| annotation_scanvi_collapsedOPC | −0.402 | 0.542 | −1.477 | 0.674 | −0.741 | 99 | 0.4603 | |
| annotation_scanvi_collapsedOligodendrocyte | −0.524 | 0.542 | −1.599 | 0.551 | −0.967 | 99 | 0.336 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| ANOVA (Type III tests) | |||||
| model term | df1 | df2 | F_ratio | p_value | |
|---|---|---|---|---|---|
| Age_End | 1 | 94 | 4.934 | 0.02873 | * |
| Condition | 1 | 94 | 1.532 | 0.2189 | |
| annotation_scanvi_collapsed | 5 | 94 | 1.030 | 0.4049 | |
| Condition:annotation_scanvi_collapsed | 5 | 94 | 1.987 | 0.08766 | . |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||
| Formula: lm(Senescent_Cell_Burden~Condition+Age_End) | ||||||||
| Number of observations: 107 Confidence intervals: parametric --------------------- R-squared: 0.0657 R-squared-adj: 0.0478 F(2, 104) = 3.659, p = 0.0291 Log-likelihood: -202 AIC: 412 | BIC: 422 Residual error: 1.623 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1.111 | 0.262 | 0.592 | 1.630 | 4.246 | 104 | <.001 | *** |
| ConditionSpace Flight | 0.403 | 0.316 | −0.222 | 1.029 | 1.278 | 104 | 0.204 | |
| Age_End37 Weeks | 0.707 | 0.316 | 0.081 | 1.333 | 2.240 | 104 | 0.02722 | * |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| Formula: lm(Senescent_Cell_Burden~Condition+Age_End+annotation_scanvi_collapsed) | ||||||||
| Number of observations: 107 Confidence intervals: parametric --------------------- R-squared: 0.1082 R-squared-adj: 0.0451 F(7, 99) = 1.716, p = 0.1139 Log-likelihood: -199 AIC: 417 | BIC: 441 Residual error: 1.626 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1.230 | 0.438 | 0.362 | 2.099 | 2.810 | 99 | 0.005968 | ** |
| ConditionSpace Flight | 0.400 | 0.316 | −0.228 | 1.027 | 1.264 | 99 | 0.2092 | |
| Age_End37 Weeks | 0.703 | 0.316 | 0.076 | 1.330 | 2.224 | 99 | 0.0284 | * |
| annotation_scanvi_collapsedEndothelial | −0.344 | 0.550 | −1.435 | 0.747 | −0.626 | 99 | 0.5331 | |
| annotation_scanvi_collapsedMicroglial | 0.103 | 0.542 | −0.973 | 1.178 | 0.189 | 99 | 0.8502 | |
| annotation_scanvi_collapsedNeuron | 0.462 | 0.542 | −0.614 | 1.537 | 0.852 | 99 | 0.3963 | |
| annotation_scanvi_collapsedOPC | −0.402 | 0.542 | −1.477 | 0.674 | −0.741 | 99 | 0.4603 | |
| annotation_scanvi_collapsedOligodendrocyte | −0.524 | 0.542 | −1.599 | 0.551 | −0.967 | 99 | 0.336 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| Formula: lm(Senescent_Cell_Burden~Age_End+Condition*annotation_scanvi_collapsed) | ||||||||
| Number of observations: 107 Confidence intervals: parametric --------------------- R-squared: 0.1934 R-squared-adj: 0.0905 F(12, 94) = 1.879, p = 0.0468 Log-likelihood: -194 AIC: 416 | BIC: 453 Residual error: 1.587 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1.036 | 0.546 | −0.048 | 2.121 | 1.897 | 94 | 0.0609 | . |
| Age_End37 Weeks | 0.685 | 0.309 | 0.073 | 1.298 | 2.221 | 94 | 0.02873 | * |
| ConditionSpace Flight | 0.805 | 0.749 | −0.682 | 2.291 | 1.075 | 94 | 0.2851 | |
| annotation_scanvi_collapsedEndothelial | 0.792 | 0.748 | −0.693 | 2.277 | 1.059 | 94 | 0.2922 | |
| annotation_scanvi_collapsedMicroglial | 0.366 | 0.748 | −1.120 | 1.851 | 0.489 | 94 | 0.6262 | |
| annotation_scanvi_collapsedNeuron | 0.033 | 0.748 | −1.452 | 1.518 | 0.044 | 94 | 0.9652 | |
| annotation_scanvi_collapsedOPC | −0.334 | 0.748 | −1.819 | 1.151 | −0.447 | 94 | 0.6562 | |
| annotation_scanvi_collapsedOligodendrocyte | −0.352 | 0.748 | −1.837 | 1.133 | −0.470 | 94 | 0.6391 | |
| ConditionSpace Flight:annotation_scanvi_collapsedEndothelial | −2.390 | 1.074 | −4.523 | −0.257 | −2.225 | 94 | 0.02847 | * |
| ConditionSpace Flight:annotation_scanvi_collapsedMicroglial | −0.526 | 1.058 | −2.626 | 1.574 | −0.497 | 94 | 0.6202 | |
| ConditionSpace Flight:annotation_scanvi_collapsedNeuron | 0.858 | 1.058 | −1.242 | 2.958 | 0.811 | 94 | 0.4194 | |
| ConditionSpace Flight:annotation_scanvi_collapsedOPC | −0.135 | 1.058 | −2.236 | 1.965 | −0.128 | 94 | 0.8984 | |
| ConditionSpace Flight:annotation_scanvi_collapsedOligodendrocyte | −0.344 | 1.058 | −2.444 | 1.756 | −0.325 | 94 | 0.7457 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| Analysis of Deviance Table | ||||||||||
| Model 1: lm(Senescent_Cell_Burden~Condition+Age_End) Model 2: lm(Senescent_Cell_Burden~Condition+Age_End+annotation_scanvi_collapsed) Model 3: lm(Senescent_Cell_Burden~Age_End+Condition*annotation_scanvi_collapsed) |
||||||||||
| AIC | BIC | logLik | Res_Df | RSS | Df | Sum of Sq | F | Pr(>F) | ||
|---|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 412.3 | 423 | −202.2 | 104.00 | 274.10 | |||||
| 2.00 | 417.3 | 441.4 | −199.7 | 99.00 | 261.64 | 5.00 | 12.46 | 0.99 | 0.428 | |
| 3.00 | 416.6 | 454 | −194.3 | 94.00 | 236.64 | 5.00 | 25.01 | 1.99 | 0.0877 | . |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||||
Just Age as Predictor¶
In [ ]:
# model_cac_mlm_i3_a = lmer(
# f"senscore ~ {col_age} * {col_celltype} + (1 | {col_sample})",
# data=r_rna) # set up model
# model_cac_mlm_i3_a.set_factors(factors)
# model_cac_mlm_i3_a.set_transforms({"senscore": "zscore"})
# model_cac_mlm_i3_a.fit()
# model_cac_mlm_i3_a.summary().show()
# fff = str(f"Senescent_Cell ~ Aged * {col_celltype} + "
# "(1 | sample)")
# glmmod_ct2a = glmer(fff, data=r_rna, family="binomial")
# glmmod_ct2a.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
# glmmod_ct2a.summary().show()
Lower SnC Threshold¶
In [17]:
# Column for Lower Threshold Classification
chp = f"Senescent_Cell_{use_metric}_{p_h}" # column name
# Cell Type Random Effects
fff = str(f"{chp} ~ Aged * Spaceflight"
f" + (1 | {col_sample}) + (1 + Spaceflight + Aged || CT_)")
glmmod_lenient_thresh = glmer(fff, data=r_rna, family="binomial")
glmmod_lenient_thresh.fit(exponentiate=True, ncpus=ncpus) # => odds
glmmod_lenient_thresh.summary().show()
# Cell Type as Moderator
fff = str(f"{chp} ~ Aged * Spaceflight * CT_ + (1 | {col_sample})")
glmmod_lenient_thresh_ct = glmer(fff, data=r_rna, family="binomial")
glmmod_lenient_thresh_ct.fit(exponentiate=True, ncpus=ncpus) # => odds
glmmod_lenient_thresh_ct.summary().show()
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
| Formula: glmer(Senescent_Cell_senmayo_3~Aged*Spaceflight+(1|sample)+(1+Spaceflight+Aged||CT_)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -20377 AIC: 40771 | BIC: 40847 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.472 | |||||||
| CT_-sd | (Intercept) | 0.000 | |||||||
| CT_.1-sd | Spaceflight | 0.076 | |||||||
| CT_.2-sd | Aged | 0.206 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.027 | 0.019 | 0.039 | 0.005 | −19.267 | inf | <.001 | *** | |
| Aged | 1.624 | 0.919 | 2.870 | 0.472 | 1.668 | inf | 0.09525 | . | |
| Spaceflight | 1.966 | 1.112 | 3.474 | 0.571 | 2.326 | inf | 0.02003 | * | |
| Aged:Spaceflight | 0.775 | 0.375 | 1.603 | 0.287 | −0.688 | inf | 0.4917 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
R messages: Convergence status : [1] FALSE attr(,"gradient") [1] 0.002085284 R messages: Convergence status : [1] FALSE attr(,"gradient") [1] 0.002085284
| Formula: glmer(Senescent_Cell_senmayo_3~Aged*Spaceflight*CT_+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -20360 AIC: 40770 | BIC: 41008 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.472 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.027 | 0.017 | 0.044 | 0.007 | −14.918 | inf | <.001 | *** | |
| Aged | 1.400 | 0.697 | 2.811 | 0.498 | 0.946 | inf | 0.344 | ||
| Spaceflight | 2.027 | 1.001 | 4.107 | 0.730 | 1.961 | inf | 0.04984 | * | |
| CT_Endothelial | 0.858 | 0.496 | 1.486 | 0.240 | −0.546 | inf | 0.5853 | ||
| CT_Microglial | 0.965 | 0.634 | 1.467 | 0.206 | −0.168 | inf | 0.8664 | ||
| CT_Neuron | 0.953 | 0.750 | 1.211 | 0.117 | −0.395 | inf | 0.693 | ||
| CT_OPC | 0.857 | 0.588 | 1.249 | 0.165 | −0.804 | inf | 0.4217 | ||
| CT_Oligodendrocyte | 1.073 | 0.822 | 1.401 | 0.146 | 0.522 | inf | 0.602 | ||
| Aged:Spaceflight | 1.149 | 0.427 | 3.088 | 0.580 | 0.274 | inf | 0.7837 | ||
| Aged:CT_Endothelial | 2.331 | 1.011 | 5.375 | 0.994 | 1.986 | inf | 0.04708 | * | |
| Aged:CT_Microglial | 1.144 | 0.638 | 2.048 | 0.340 | 0.451 | inf | 0.6518 | ||
| Aged:CT_Neuron | 1.609 | 1.157 | 2.238 | 0.271 | 2.827 | inf | 0.004693 | ** | |
| Aged:CT_OPC | 0.925 | 0.520 | 1.649 | 0.273 | −0.263 | inf | 0.7926 | ||
| Aged:CT_Oligodendrocyte | 0.779 | 0.532 | 1.141 | 0.151 | −1.284 | inf | 0.1993 | ||
| Spaceflight:CT_Endothelial | 0.398 | 0.050 | 3.145 | 0.420 | −0.874 | inf | 0.382 | ||
| Spaceflight:CT_Microglial | 0.954 | 0.531 | 1.714 | 0.285 | −0.158 | inf | 0.8747 | ||
| Spaceflight:CT_Neuron | 0.956 | 0.669 | 1.366 | 0.174 | −0.248 | inf | 0.8045 | ||
| Spaceflight:CT_OPC | 1.157 | 0.655 | 2.043 | 0.336 | 0.503 | inf | 0.6153 | ||
| Spaceflight:CT_Oligodendrocyte | 0.839 | 0.567 | 1.242 | 0.168 | −0.877 | inf | 0.3807 | ||
| Aged:Spaceflight:CT_Endothelial | 0.755 | 0.076 | 7.555 | 0.888 | −0.239 | inf | 0.8113 | ||
| Aged:Spaceflight:CT_Microglial | 0.735 | 0.329 | 1.640 | 0.301 | −0.753 | inf | 0.4516 | ||
| Aged:Spaceflight:CT_Neuron | 0.609 | 0.378 | 0.983 | 0.149 | −2.031 | inf | 0.04229 | * | |
| Aged:Spaceflight:CT_OPC | 0.693 | 0.304 | 1.580 | 0.291 | −0.872 | inf | 0.3834 | ||
| Aged:Spaceflight:CT_Oligodendrocyte | 0.894 | 0.519 | 1.541 | 0.248 | −0.403 | inf | 0.6872 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Perturbation Distance¶
In [19]:
print(f"\n\n{'=' * 80}\nOverall Distance\n{'=' * 80}\n\n")
df_distance, figs_distance = scflow.ax.analyze_perturbation_distance(
self.rna, col_condition=[col_age, col_condition, col_batch])
print(f"\n\n{'=' * 80}\nJust Senescence Genes\n{'=' * 80}\n\n")
df_distance_snc, figs_distance_snc = scflow.ax.analyze_perturbation_distance(
self.rna[:, genes], col_condition=[
col_age, col_condition, col_batch])
Output()
================================================================================ Overall Distance ================================================================================
Output()
Output()
================================================================================ Just Senescence Genes ================================================================================
Output()
Output()
Output()
Composition Analysis¶
Cell Type¶
In [ ]:
# print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
# out_ctc = scflow.ax.analyze_composition(
# self.rna, col_celltype, col_batch, col_sample=col_sample,
# palette=palette[col_batch],
# formula=None, key_modality="coda", reference_cell_type="automatic",
# absence_threshold=0.1, est_fdr=0.1, level_order=[keys[
# col_batch]["key_control"]] + keys[col_batch]["key_treatment"])
# plt.show()
# print(f"\n\n{'=' * 80}\nAged Subset\n{'=' * 80}\n\n")
# out_ctc_sf = scflow.ax.analyze_composition(
# self.rna[self.rna.obs[col_age] != keys[col_age]["key_control"]],
# col_celltype, col_condition, col_sample=col_sample,
# formula=None, key_modality="coda", reference_cell_type="automatic",
# absence_threshold=0.1, est_fdr=0.1, level_order=[keys[col_condition][
# "key_control"]] + [keys[col_condition]["key_treatment"]])
Senescent Cells¶
Tree-Aggregated¶
In [178]:
# Setup
# col_celltype_hierarchy = [
# f"{col_celltype}_hierarchical", "SnC_hierarchy"]
col_celltype_hierarchy = [
"Senescent_Cell", f"{col_celltype}_hierarchical", "SnC_hierarchy",
"Senescent_Cell_Label_by_Type"]
# col_celltype_hierarchy = [
# "Senescent_Cell_Label", f"SnC_hierarchy",
# "Senescent_Cell_Label_by_Type"]
ref_celltype = self.rna.obs[col_celltype_hierarchy[-1]].value_counts(
).index.values[0]
form_c = " + ".join([col_condition, col_age]) + " + " + " * ".join([
col_condition, col_age])
# By SF vs. GC (No Age)
_ = scflow.ax.analyze_composition_tree(
self.rna, col_celltype_hierarchy[-1], col_condition,
col_sample=col_sample, formula=None, seed=0, est_fdr=0.2,
reference_cell_type=ref_celltype, num_warmup=2000,
col_celltype_hierarchy=col_celltype_hierarchy)
# By SF & Age
_ = scflow.ax.analyze_composition_tree(
self.rna, col_celltype_hierarchy[-1], [col_condition, col_age],
formula=form_c, col_sample=col_sample, seed=0,
reference_cell_type=ref_celltype, est_fdr=0.2,
col_celltype_hierarchy=col_celltype_hierarchy)
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [01:54<00:00, 104.99it/s, 31 steps of size 1.63e-01
Compositional Analysis summary ┌──────────────────────────────────┬──────────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├──────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 12 cell types │ │ Reference cell type │ Neuron │ │ Formula │ Condition │ │ Reference index │ [3, 0] │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 93.3% │ └──────────────────────────────────┴──────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 2.299 2.014 2.588 0.155 410.043 │ │ Endothelial -0.027 -0.487 0.458 0.250 40.055 │ │ Microglial 1.605 1.310 1.895 0.158 204.847 │ │ Neuron 4.495 4.265 4.739 0.127 3685.868 │ │ OPC 1.646 1.337 1.938 0.160 213.420 │ │ Oligodendrocyte 3.227 2.967 3.476 0.137 1037.180 │ │ SnC Astrocyte -0.818 -1.263 -0.349 0.247 18.161 │ │ SnC Endothelial -1.451 -1.914 -0.973 0.255 9.643 │ │ SnC Microglial -1.078 -1.549 -0.628 0.248 14.003 │ │ SnC Neuron 0.492 0.117 0.853 0.196 67.307 │ │ SnC OPC -1.221 -1.705 -0.767 0.250 12.137 │ │ SnC Oligodendrocyte -0.485 -0.906 -0.036 0.234 25.337 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Effect Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.000 410.043 0.000 │ │ Endothelial 0.000 40.055 0.000 │ │ Microglial 0.000 204.847 0.000 │ │ Neuron 0.000 3685.868 0.000 │ │ OPC 0.000 213.420 0.000 │ │ Oligodendrocyte 0.000 1037.180 0.000 │ │ SnC Astrocyte 0.000 18.161 0.000 │ │ SnC Endothelial 0.000 9.643 0.000 │ │ SnC Microglial 0.000 14.003 0.000 │ │ SnC Neuron 0.000 67.307 0.000 │ │ SnC OPC 0.000 12.137 0.000 │ │ SnC Oligodendrocyte 0.000 25.337 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Median HDI 3% HDI 97% SD │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.043 -0.185 0.226 0.108 │ │ Endothelial 0.012 -0.477 0.220 0.200 │ │ Microglial 0.069 -0.072 0.275 0.091 │ │ Neuron 0.000 0.000 0.000 0.000 │ │ OPC 0.083 -0.096 0.279 0.098 │ │ Oligodendrocyte 0.105 -0.041 0.254 0.079 │ │ SnC Astrocyte 0.212 -0.074 0.551 0.167 │ │ SnC Endothelial 0.136 -0.130 0.435 0.148 │ │ SnC Microglial 0.144 -0.082 0.461 0.144 │ │ SnC Neuron 0.186 -0.054 0.528 0.162 │ │ SnC OPC 0.214 -0.100 0.542 0.171 │ │ SnC Oligodendrocyte 0.218 -0.076 0.550 0.167 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Nodes │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Condition[T.Space Flight]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ False 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ True 0.00 0.08 -0.06 0.30 0.10 0.12 False │ │ Macroglia 0.00 0.04 -0.08 0.14 0.06 0.12 False │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Microglial 0.00 0.07 -0.07 0.28 0.09 0.12 False │ │ Endothelial 0.00 0.01 -0.48 0.22 0.20 0.12 False │ │ SnC Macroglia 0.00 0.06 -0.10 0.24 0.09 0.12 False │ │ SnC Neuron 0.00 0.08 -0.10 0.39 0.14 0.12 False │ │ SnC Endothelial 0.00 0.05 -0.15 0.26 0.11 0.12 False │ │ SnC Microglial 0.00 0.05 -0.14 0.27 0.10 0.12 False │ │ Oligodendrocyte 0.00 0.07 -0.04 0.21 0.07 0.12 False │ │ OPC 0.00 0.05 -0.12 0.20 0.08 0.12 False │ │ Astrocyte 0.00 0.02 -0.21 0.16 0.10 0.12 False │ │ SnC OPC 0.00 0.05 -0.14 0.27 0.10 0.12 False │ │ SnC Oligodendrocyte 0.00 0.05 -0.12 0.26 0.10 0.12 False │ │ SnC Astrocyte 0.00 0.05 -0.14 0.26 0.10 0.12 False │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 11000/11000 [02:00<00:00, 91.42it/s, 31 steps of size 1.18e-01.
Compositional Analysis summary ┌──────────────────────────────────┬──────────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├──────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 12 cell types │ │ Reference cell type │ Neuron │ │ Formula │ Condition + Age_End + Condition * Age_End │ │ Reference index │ [3, 0] │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 94.0% │ └──────────────────────────────────┴──────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 2.613 2.322 2.929 0.161 460.187 │ │ Endothelial 0.131 -0.362 0.646 0.268 38.461 │ │ Microglial 1.780 1.458 2.106 0.172 200.063 │ │ Neuron 4.642 4.393 4.875 0.129 3500.401 │ │ OPC 1.953 1.615 2.257 0.170 237.848 │ │ Oligodendrocyte 3.549 3.272 3.815 0.145 1173.367 │ │ SnC Astrocyte -0.753 -1.218 -0.271 0.255 15.889 │ │ SnC Endothelial -1.423 -1.919 -0.898 0.270 8.131 │ │ SnC Microglial -1.034 -1.526 -0.555 0.259 11.997 │ │ SnC Neuron 0.554 0.176 0.932 0.201 58.711 │ │ SnC OPC -1.177 -1.670 -0.672 0.268 10.398 │ │ SnC Oligodendrocyte -0.403 -0.861 0.068 0.249 22.548 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Effect Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.000 460.187 0.000 │ │ Endothelial 0.000 38.461 0.000 │ │ Microglial 0.000 200.063 0.000 │ │ Neuron 0.000 3500.401 0.000 │ │ OPC 0.000 237.848 0.000 │ │ Oligodendrocyte 0.000 1173.367 0.000 │ │ SnC Astrocyte 0.000 15.889 0.000 │ │ SnC Endothelial 0.000 8.131 0.000 │ │ SnC Microglial 0.000 11.997 0.000 │ │ SnC Neuron 0.000 58.711 0.000 │ │ SnC OPC 0.000 10.398 0.000 │ │ SnC Oligodendrocyte 0.000 22.548 0.000 │ │ Age_EndT.37 Weeks Astrocyte -0.328 364.769 -0.335 │ │ Endothelial 0.000 42.320 0.138 │ │ Microglial 0.000 220.140 0.138 │ │ Neuron 0.000 3851.679 0.138 │ │ OPC -0.328 188.531 -0.335 │ │ Oligodendrocyte -0.328 930.074 -0.335 │ │ SnC Astrocyte 0.000 17.484 0.138 │ │ SnC Endothelial 0.000 8.947 0.138 │ │ SnC Microglial 0.000 13.201 0.138 │ │ SnC Neuron 0.000 64.603 0.138 │ │ SnC OPC 0.000 11.442 0.138 │ │ SnC Oligodendrocyte 0.000 24.810 0.138 │ │ T.Space Flight:Age_EndT.37 Weeks Astrocyte 0.000 460.187 0.000 │ │ Endothelial 0.000 38.461 0.000 │ │ Microglial 0.000 200.063 0.000 │ │ Neuron 0.000 3500.401 0.000 │ │ OPC 0.000 237.848 0.000 │ │ Oligodendrocyte 0.000 1173.367 0.000 │ │ SnC Astrocyte 0.000 15.889 0.000 │ │ SnC Endothelial 0.000 8.131 0.000 │ │ SnC Microglial 0.000 11.997 0.000 │ │ SnC Neuron 0.000 58.711 0.000 │ │ SnC OPC 0.000 10.398 0.000 │ │ SnC Oligodendrocyte 0.000 22.548 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Median HDI 3% HDI 97% SD │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.012 -0.189 0.169 0.092 │ │ Endothelial -0.025 -0.555 0.131 0.208 │ │ Microglial 0.032 -0.080 0.286 0.097 │ │ Neuron 0.000 0.000 0.000 0.000 │ │ OPC 0.037 -0.127 0.214 0.088 │ │ Oligodendrocyte 0.076 -0.051 0.246 0.079 │ │ SnC Astrocyte 0.116 -0.152 0.462 0.162 │ │ SnC Endothelial 0.071 -0.178 0.400 0.146 │ │ SnC Microglial 0.078 -0.141 0.411 0.146 │ │ SnC Neuron 0.123 -0.094 0.502 0.167 │ │ SnC OPC 0.119 -0.151 0.473 0.166 │ │ SnC Oligodendrocyte 0.121 -0.155 0.460 0.162 │ │ Age_EndT.37 Weeks Astrocyte -0.321 -0.533 -0.071 0.121 │ │ Endothelial 0.002 -0.300 0.170 0.129 │ │ Microglial -0.017 -0.319 0.104 0.119 │ │ Neuron 0.000 0.000 0.000 0.000 │ │ OPC -0.319 -0.546 -0.049 0.130 │ │ Oligodendrocyte -0.348 -0.542 -0.158 0.102 │ │ SnC Astrocyte 0.040 -0.263 0.313 0.147 │ │ SnC Endothelial 0.038 -0.206 0.278 0.124 │ │ SnC Microglial 0.036 -0.206 0.273 0.122 │ │ SnC Neuron 0.078 -0.135 0.420 0.152 │ │ SnC OPC 0.041 -0.261 0.322 0.147 │ │ SnC Oligodendrocyte 0.030 -0.277 0.318 0.151 │ │ T.Space Flight:Age_EndT.37 Weeks Astrocyte 0.011 -0.224 0.177 0.103 │ │ Endothelial 0.003 -0.305 0.162 0.129 │ │ Microglial 0.012 -0.164 0.168 0.081 │ │ Neuron 0.000 0.000 0.000 0.000 │ │ OPC 0.031 -0.188 0.225 0.104 │ │ Oligodendrocyte 0.030 -0.148 0.205 0.089 │ │ SnC Astrocyte 0.065 -0.219 0.384 0.153 │ │ SnC Endothelial 0.047 -0.189 0.329 0.131 │ │ SnC Microglial 0.048 -0.193 0.318 0.131 │ │ SnC Neuron 0.076 -0.131 0.427 0.151 │ │ SnC OPC 0.067 -0.236 0.373 0.155 │ │ SnC Oligodendrocyte 0.062 -0.246 0.356 0.153 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Nodes │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Age_End[T.37 Weeks]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ False 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ True 0.00 0.02 -0.11 0.19 0.07 0.15 False │ │ Macroglia -0.33 -0.33 -0.55 -0.08 0.12 0.15 True │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Microglial 0.00 -0.02 -0.32 0.10 0.12 0.15 False │ │ Endothelial 0.00 0.00 -0.30 0.17 0.13 0.15 False │ │ SnC Macroglia 0.00 0.01 -0.21 0.16 0.09 0.15 False │ │ SnC Neuron 0.00 0.04 -0.10 0.40 0.14 0.15 False │ │ SnC Endothelial 0.00 0.01 -0.18 0.21 0.10 0.15 False │ │ SnC Microglial 0.00 0.01 -0.20 0.19 0.10 0.15 False │ │ Oligodendrocyte 0.00 0.00 -0.22 0.12 0.09 0.15 False │ │ OPC 0.00 0.01 -0.16 0.15 0.08 0.15 False │ │ Astrocyte 0.00 0.01 -0.16 0.15 0.07 0.15 False │ │ SnC OPC 0.00 0.01 -0.19 0.22 0.10 0.15 False │ │ SnC Oligodendrocyte 0.00 0.01 -0.22 0.19 0.11 0.15 False │ │ SnC Astrocyte 0.00 0.01 -0.19 0.21 0.10 0.15 False │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Condition[T.Space Flight]:Age_End[T.37 Weeks]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ False 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ True 0.00 0.03 -0.10 0.24 0.09 0.15 False │ │ Macroglia 0.00 0.01 -0.14 0.13 0.07 0.15 False │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Microglial 0.00 0.01 -0.16 0.17 0.08 0.15 False │ │ Endothelial 0.00 0.00 -0.30 0.16 0.13 0.15 False │ │ SnC Macroglia 0.00 0.01 -0.16 0.19 0.09 0.15 False │ │ SnC Neuron 0.00 0.03 -0.13 0.33 0.13 0.15 False │ │ SnC Endothelial 0.00 0.01 -0.18 0.22 0.10 0.15 False │ │ SnC Microglial 0.00 0.01 -0.18 0.21 0.10 0.15 False │ │ Oligodendrocyte 0.00 0.02 -0.11 0.16 0.07 0.15 False │ │ OPC 0.00 0.02 -0.15 0.19 0.08 0.15 False │ │ Astrocyte 0.00 0.01 -0.18 0.15 0.08 0.15 False │ │ SnC OPC 0.00 0.01 -0.19 0.21 0.10 0.15 False │ │ SnC Oligodendrocyte 0.00 0.01 -0.18 0.21 0.10 0.15 False │ │ SnC Astrocyte 0.00 0.01 -0.18 0.20 0.10 0.15 False │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Condition[T.Space Flight]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ False 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ True 0.00 0.04 -0.07 0.34 0.11 0.15 False │ │ Macroglia 0.00 0.02 -0.08 0.14 0.06 0.15 False │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Microglial 0.00 0.03 -0.08 0.29 0.10 0.15 False │ │ Endothelial 0.00 -0.03 -0.56 0.13 0.21 0.15 False │ │ SnC Macroglia 0.00 0.02 -0.14 0.22 0.09 0.15 False │ │ SnC Neuron 0.00 0.04 -0.13 0.38 0.14 0.15 False │ │ SnC Endothelial 0.00 0.01 -0.19 0.20 0.10 0.15 False │ │ SnC Microglial 0.00 0.02 -0.17 0.22 0.10 0.15 False │ │ Oligodendrocyte 0.00 0.04 -0.06 0.22 0.07 0.15 False │ │ OPC 0.00 0.01 -0.13 0.17 0.07 0.15 False │ │ Astrocyte 0.00 -0.00 -0.20 0.12 0.08 0.15 False │ │ SnC OPC 0.00 0.02 -0.16 0.23 0.10 0.15 False │ │ SnC Oligodendrocyte 0.00 0.02 -0.17 0.22 0.10 0.15 False │ │ SnC Astrocyte 0.00 0.02 -0.17 0.21 0.10 0.15 False │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:1990: PerformanceWarning: indexing past lexsort depth may impact performance. node_effs = data.uns["scCODA_params"]["node_df"].loc[(covariate + "_node",),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:2000: PerformanceWarning: indexing past lexsort depth may impact performance. leaf_effs = eff_df.loc[(covariate,),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:1990: PerformanceWarning: indexing past lexsort depth may impact performance. node_effs = data.uns["scCODA_params"]["node_df"].loc[(covariate + "_node",),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:2000: PerformanceWarning: indexing past lexsort depth may impact performance. leaf_effs = eff_df.loc[(covariate,),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:1990: PerformanceWarning: indexing past lexsort depth may impact performance. node_effs = data.uns["scCODA_params"]["node_df"].loc[(covariate + "_node",),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:2000: PerformanceWarning: indexing past lexsort depth may impact performance. leaf_effs = eff_df.loc[(covariate,),].copy()
Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png
<Figure size 0x300 with 0 Axes>
scCoda¶
Just by Condition (By Cell Type)¶
In [ ]:
# for x in self.rna.obs[col_celltype].unique():
# r_s, num_samples, num_warmup = 0, 10000, 2000 # defaults
# if "annotation_scanvi" in col_celltype: # avoid bad acceptance rates
# r_s = 16 if x == "OPC" else 8675309 if x in [
# "Microglial"] else r_s # different random seed
# if x == "Endothelial":
# r_s, num_samples, num_warmups = 7654321, 14000, 2000
# print(f"Seed = {r_s}; {num_samples} samples; {num_samples} warmup")
# out = scflow.ax.analyze_composition(
# self.rna[self.rna.obs[col_celltype] == x],
# "Senescent_Cell_Label_by_Type", col_condition,
# col_sample=col_sample, formula=None, key_modality="coda",
# full_hmc=False, palette=palette[col_condition], seed=r_s,
# num_samples=10000, num_warmup=2000,
# reference_cell_type=x, absence_threshold=0.1, est_fdr=0.2,
# level_order=[keys[col_condition]["key_control"], keys[
# col_condition]["key_treatment"]])
# f_sccoda_out = f"outputs/sccoda_results_{col_celltype}_{x}_sfgc.pkl"
# if overwrite is True or os.path.exists(f_sccoda_out) is False:
# with open(f_sccoda_out, "wb") as f:
# pickle.dump(out[0], f)
# # out[1].write_h5mu(
# # f"outputs/sccoda_data_{col_celltype}_{x}_sfgc.h5mu")
# plt.show()
# del out
By Condition & Age¶
In [ ]:
================================================================================
By Cell Type (by Group)
================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [41:24<00:00, 4.83it/s, 1023 steps of size 2.22e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ OPC │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 81.0% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ OPC 33.349 33.349 33.349 0.000 199.333 │ │ SnC OPC 2.026 2.026 2.026 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks OPC 0.000 199.333 0.000 │ │ SnC OPC 0.000 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks OPC 0.000 199.333 0.000 │ │ SnC OPC -0.001 0.000 -0.002 │ │ GroupT.Space Flight | 37 Weeks OPC 0.000 0.000 -124.658 │ │ SnC OPC 117.730 199.333 45.190 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC -0.001 -0.001 0.000 1.000 │ │ GroupT.Space Flight | 37 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC 117.691 117.691 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
OPC
SnC OPC * *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [38:40<00:00, 5.17it/s, 1023 steps of size 1.56e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 17 samples, 2 cell types │ │ Reference cell type │ Endothelial │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 90.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Endothelial 35.456 35.456 35.456 0.000 51.588 │ │ SnC Endothelial 3.752 3.752 3.752 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Endothelial 0.000 51.588 0.000 │ │ SnC Endothelial 0.000 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks Endothelial 0.000 51.588 0.000 │ │ SnC Endothelial 0.000 0.000 0.000 │ │ GroupT.Space Flight | 37 Weeks Endothelial 0.000 0.000 -26.114 │ │ SnC Endothelial 49.805 51.588 45.739 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 37 Weeks Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial 49.805 49.805 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
Endothelial
SnC Endothelial *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [03:41<00:00, 54.10it/s, 63 steps of size 4.11e-02.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Astrocyte │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.835 │ │ Spike-and-slab threshold │ 0.835 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 79.5% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 5.890 4.245 7.672 0.972 389.978 │ │ SnC Astrocyte 1.724 0.285 3.450 0.904 6.050 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Astrocyte 0.000 389.978 0.000 │ │ SnC Astrocyte 0.000 6.050 0.000 │ │ GroupT.Space Flight | 20 Weeks Astrocyte 0.000 389.978 0.000 │ │ SnC Astrocyte 0.000 6.050 0.000 │ │ GroupT.Space Flight | 37 Weeks Astrocyte 0.000 384.531 -0.020 │ │ SnC Astrocyte 0.656 11.497 0.926 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -0.493 0.575 0.163 0.363 │ │ GroupT.Space Flight | 20 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -0.342 0.699 0.198 0.433 │ │ GroupT.Space Flight | 37 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -0.014 1.212 0.394 0.835 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ================================================== Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks] Cell Type Astrocyte SnC Astrocyte * ==========================================================================================================================
In [182]:
print(f"\n\n{'=' * 80}\nBy Cell Type (by Group)\n{'=' * 80}\n\n")
for x in self.rna.obs[col_celltype].unique():
r_s = 1618
if "annotation_scanvi" in col_celltype:
r_s = 32145 if x in ["Neuron"] else 16 if x in [
"Microglia", "OPC"] else r_s
out = scflow.ax.analyze_composition(
self.rna[self.rna.obs[col_celltype] == x],
"Senescent_Cell_Label_by_Type", col_batch, col_sample=col_sample,
formula=None, key_modality="coda", full_hmc=False,
palette=palette[col_batch], seed=r_s,
num_samples=10000, num_warmup=2000,
reference_cell_type=x, absence_threshold=0.1, est_fdr=0.2,
level_order=[keys[col_batch]["key_control"]] + keys[
col_batch]["key_treatment"])
f_sccoda_out = f"outputs/sccoda_results_{col_celltype}_{x}.pkl"
if overwrite is True or os.path.exists(f_sccoda_out) is False:
with open(f_sccoda_out, "wb") as f:
pickle.dump(out[0], f)
# out[1].write_h5mu(f"outputs/sccoda_data_{col_celltype}_{x}.h5mu")
plt.show()
del out
# Reload
# out_ctc_ct = {}
# for x in self.rna.obs[col_celltype].unique():
# with open(f"outputs/sccoda_results_{col_celltype}_{x}.pkl", "rb") as f:
# tmp = pickle.load(f)
# out_ctc_ct[x] = [tmp, mudata.read(
# f"outputs/sccoda_data_{col_celltype}_{x}.h5mu")]
# del tmp
================================================================================ By Cell Type (by Group) ================================================================================
sample: 100%|█| 12000/12000 [03:25<00:00, 58.30it/s, 1 steps of size 4.16e-02.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Neuron │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 76.0% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Neuron 3.979 3.222 4.725 0.395 3787.418 │ │ SnC Neuron 0.124 -0.500 0.727 0.322 80.193 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Neuron 0.000 3787.418 0.000 │ │ SnC Neuron 0.000 80.193 0.000 │ │ GroupT.Space Flight | 20 Weeks Neuron 0.000 3787.418 0.000 │ │ SnC Neuron 0.000 80.193 0.000 │ │ GroupT.Space Flight | 37 Weeks Neuron 0.000 3787.418 0.000 │ │ SnC Neuron 0.000 80.193 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Neuron 0.000 0.000 0.000 0.000 │ │ SnC Neuron -0.481 0.762 0.190 0.444 │ │ GroupT.Space Flight | 20 Weeks Neuron 0.000 0.000 0.000 0.000 │ │ SnC Neuron -0.525 0.737 0.198 0.358 │ │ GroupT.Space Flight | 37 Weeks Neuron 0.000 0.000 0.000 0.000 │ │ SnC Neuron -0.041 48.593 14.704 0.607 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
Neuron
SnC Neuron
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [03:46<00:00, 53.03it/s, 63 steps of size 4.15e-02.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Oligodendrocyte │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 76.2% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Oligodendrocyte 6.802 5.160 8.430 0.976 1026.945 │ │ SnC Oligodendrocyte 2.400 0.809 3.940 0.937 12.583 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Oligodendrocyte 0.000 1026.945 0.000 │ │ SnC Oligodendrocyte 0.000 12.583 0.000 │ │ GroupT.Space Flight | 20 Weeks Oligodendrocyte 0.000 1026.945 0.000 │ │ SnC Oligodendrocyte 0.000 12.583 0.000 │ │ GroupT.Space Flight | 37 Weeks Oligodendrocyte 0.000 1026.945 0.000 │ │ SnC Oligodendrocyte 0.000 12.583 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.689 0.175 0.178 0.395 │ │ GroupT.Space Flight | 20 Weeks Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.073 0.776 0.249 0.591 │ │ GroupT.Space Flight | 37 Weeks Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.108 0.739 0.226 0.539 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
Oligodendrocyte
SnC Oligodendrocyte
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [33:55<00:00, 5.90it/s, 1023 steps of size 1.85e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Microglial │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 87.2% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Microglial 35.771 35.771 35.771 0.000 186.722 │ │ SnC Microglial 2.377 2.377 2.377 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Microglial 0.000 0.000 -67.961 │ │ SnC Microglial 80.501 186.722 48.177 │ │ GroupT.Space Flight | 20 Weeks Microglial 0.000 186.722 0.000 │ │ SnC Microglial 0.000 0.000 0.000 │ │ GroupT.Space Flight | 37 Weeks Microglial 0.000 186.722 0.000 │ │ SnC Microglial 0.030 0.000 0.044 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial 80.501 80.501 0.000 1.000 │ │ GroupT.Space Flight | 20 Weeks Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 37 Weeks Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial 0.030 0.030 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
Microglial
SnC Microglial * *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [37:43<00:00, 5.30it/s, 1023 steps of size 2.22e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ OPC │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 81.0% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ OPC 33.349 33.349 33.349 0.000 199.333 │ │ SnC OPC 2.026 2.026 2.026 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks OPC 0.000 199.333 0.000 │ │ SnC OPC 0.000 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks OPC 0.000 199.333 0.000 │ │ SnC OPC -0.001 0.000 -0.002 │ │ GroupT.Space Flight | 37 Weeks OPC 0.000 0.000 -124.658 │ │ SnC OPC 117.730 199.333 45.190 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC -0.001 -0.001 0.000 1.000 │ │ GroupT.Space Flight | 37 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC 117.691 117.691 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
OPC
SnC OPC * *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [38:58<00:00, 5.13it/s, 1023 steps of size 1.56e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 17 samples, 2 cell types │ │ Reference cell type │ Endothelial │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 90.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Endothelial 35.456 35.456 35.456 0.000 51.588 │ │ SnC Endothelial 3.752 3.752 3.752 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Endothelial 0.000 51.588 0.000 │ │ SnC Endothelial 0.000 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks Endothelial 0.000 51.588 0.000 │ │ SnC Endothelial 0.000 0.000 0.000 │ │ GroupT.Space Flight | 37 Weeks Endothelial 0.000 0.000 -26.114 │ │ SnC Endothelial 49.805 51.588 45.739 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 20 Weeks Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 37 Weeks Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial 49.805 49.805 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks]
Cell Type
Endothelial
SnC Endothelial *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [03:40<00:00, 54.50it/s, 63 steps of size 4.11e-02.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Astrocyte │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.835 │ │ Spike-and-slab threshold │ 0.835 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 79.5% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 5.890 4.245 7.672 0.972 389.978 │ │ SnC Astrocyte 1.724 0.285 3.450 0.904 6.050 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Astrocyte 0.000 389.978 0.000 │ │ SnC Astrocyte 0.000 6.050 0.000 │ │ GroupT.Space Flight | 20 Weeks Astrocyte 0.000 389.978 0.000 │ │ SnC Astrocyte 0.000 6.050 0.000 │ │ GroupT.Space Flight | 37 Weeks Astrocyte 0.000 384.531 -0.020 │ │ SnC Astrocyte 0.656 11.497 0.926 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 37 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -0.493 0.575 0.163 0.363 │ │ GroupT.Space Flight | 20 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -0.342 0.699 0.198 0.433 │ │ GroupT.Space Flight | 37 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -0.014 1.212 0.394 0.835 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ================================================== Covariate Group[T.Ground Control | 37 Weeks] Group[T.Space Flight | 20 Weeks] Group[T.Space Flight | 37 Weeks] Cell Type Astrocyte SnC Astrocyte * ==========================================================================================================================
Lower SnC Threshold¶
In [184]:
chp = f"Senescent_Cell_Label_by_Type_{use_metric}_{p_h}" # column name
print("Cross-Tabs of Cell Counts in SnC Classifications "
f"with Top {percentile}% Threshold versus Top {p_h}%")
print(self.rna.obs[["Senescent_Cell_Label_by_Type", chp]].value_counts(
).sort_index(), "\n\n") # cross-tabs of higher vs. lower SnC threshold
# print(self.rna.obs[["Senescent_Cell_Label_by_Type", chp, col_batch]].groupby(
# col_batch).value_counts().unstack(0).replace(0, np.nan).dropna(
# how="all", axis=0).astype(int).sort_index()) # cross-tabs by group
for x in self.rna.obs[col_celltype].unique():
out = scflow.ax.analyze_composition(
self.rna[self.rna.obs[col_celltype] == x],
chp, col_condition, col_sample=col_sample,
formula=None, key_modality="coda", full_hmc=False,
palette=palette[col_condition], seed=1618,
num_samples=10000, num_warmup=2000,
reference_cell_type=x, absence_threshold=0.1, est_fdr=0.2,
level_order=[keys[col_condition]["key_control"], keys[
col_condition]["key_treatment"]])
f_sccoda = f"outputs/sccoda_results_{col_celltype}_{x}_sfgc_{p_h}pct.pkl"
if overwrite is True or os.path.exists(f_sccoda) is False:
with open(f_sccoda, "wb") as f:
pickle.dump(out[0], f)
out[1].write_h5mu(
f"outputs/sccoda_data_{col_celltype}_{x}_sfgc_{p_h}pct.h5mu")
plt.show()
del out
Cross-Tabs of Cell Counts in SnC Classifications with Top 1% Threshold versus Top 3%
Senescent_Cell_Label_by_Type Senescent_Cell_Label_by_Type_senmayo_3
Astrocyte Astrocyte 6807
SnC Astrocyte 200
Endothelial Endothelial 836
SnC Endothelial 21
Microglial Microglial 3210
SnC Microglial 87
Neuron Neuron 65616
SnC Neuron 2431
OPC OPC 3445
SnC OPC 93
Oligodendrocyte Oligodendrocyte 17988
SnC Oligodendrocyte 485
SnC Astrocyte SnC Astrocyte 121
SnC Endothelial SnC Endothelial 15
SnC Microglial SnC Microglial 63
SnC Neuron SnC Neuron 1570
SnC OPC SnC OPC 48
SnC Oligodendrocyte SnC Oligodendrocyte 238
Name: count, dtype: int64
sample: 100%|█| 12000/12000 [34:25<00:00, 5.81it/s, 1023 steps of size 1.22e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Neuron │ │ Formula │ Condition │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 92.9% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type_senmayo_3 │ │ Neuron 37.299 37.299 37.299 0.000 3867.611 │ │ SnC Neuron 0.055 0.055 0.055 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Neuron 0.000 0.000 -102.417 │ │ SnC Neuron 108.234 3867.611 53.732 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ T.Space Flight Neuron 0.000 0.000 0.000 0.000 │ │ SnC Neuron 108.226 108.226 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
================================================== Credible Effects ================================================== Covariate Condition[T.Space Flight] Cell Type Neuron SnC Neuron * ==========================================================================================================================
... storing 'rna:scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
sample: 100%|█| 12000/12000 [01:33<00:00, 128.05it/s, 1 steps of size 6.01e-02.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Oligodendrocyte │ │ Formula │ Condition │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.908 │ │ Spike-and-slab threshold │ 0.908 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 60.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type_senmayo_3 │ │ Oligodendrocyte 15.477 5.216 42.203 15.909 1039.487 │ │ SnC Oligodendrocyte 4.195 1.927 9.093 2.737 0.013 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Oligodendrocyte 0.000 0.000 -33.324 │ │ SnC Oligodendrocyte 34.380 1039.500 16.277 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ T.Space Flight Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.008 39.799 73.660 0.908 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Condition[T.Space Flight]
Cell Type
Oligodendrocyte
SnC Oligodendrocyte *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [00:26<00:00, 449.49it/s, 1 steps of size 1.14e-01.
! Acceptance rate unusually low (0.03040531919314433 < 0.5)! Results might be incorrect! Please check feasibility of results and re-run the sampling step with a different rng_key if necessary.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Microglial │ │ Formula │ Condition │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.979 │ │ Spike-and-slab threshold │ 0.979 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 3.0% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type_senmayo_3 │ │ Microglial 4.245 4.251 4.251 0.109 179.269 │ │ SnC Microglial 1.061 1.062 1.062 0.090 7.425 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Microglial 0.000 0.000 -154.907 │ │ SnC Microglial 110.598 186.694 4.652 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ T.Space Flight Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial 112.415 112.415 21.132 0.979 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ================================================== Covariate Condition[T.Space Flight] Cell Type Microglial SnC Microglial * ==========================================================================================================================
sample: 100%|█| 12000/12000 [28:53<00:00, 6.92it/s, 2 steps of size 2.90e-16.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ OPC │ │ Formula │ Condition │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 84.5% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type_senmayo_3 │ │ OPC 36.278 36.278 36.278 0.000 199.222 │ │ SnC OPC 2.366 2.366 2.366 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight OPC 0.000 0.000 -69.174 │ │ SnC OPC 81.860 199.222 48.925 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ T.Space Flight OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC 81.841 81.841 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Condition[T.Space Flight]
Cell Type
OPC
SnC OPC *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [36:01<00:00, 5.55it/s, 1023 steps of size 7.84e-1
! Acceptance rate unusually high (0.964199999960805 > 0.95)! Results might be incorrect! Please check feasibility of results and re-run the sampling step with a different rng_key if necessary.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 17 samples, 2 cell types │ │ Reference cell type │ Endothelial │ │ Formula │ Condition │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 96.4% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type_senmayo_3 │ │ Endothelial 37.166 37.166 37.166 0.000 51.500 │ │ SnC Endothelial 5.894 5.894 5.894 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Endothelial 0.000 0.000 -257.519 │ │ SnC Endothelial 209.771 51.500 45.116 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ T.Space Flight Endothelial 0.000 0.000 0.000 0.000 │ │ SnC Endothelial 209.771 209.771 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Condition[T.Space Flight]
Cell Type
Endothelial
SnC Endothelial *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [00:38<00:00, 308.21it/s, 1 steps of size 6.60e-02.
! Acceptance rate unusually low (0.08084303939918638 < 0.5)! Results might be incorrect! Please check feasibility of results and re-run the sampling step with a different rng_key if necessary.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Astrocyte │ │ Formula │ Condition │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.972 │ │ Spike-and-slab threshold │ 0.972 │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 8.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type_senmayo_3 │ │ Astrocyte 4.831 4.598 5.163 0.190 377.317 │ │ SnC Astrocyte 1.827 1.442 1.915 0.164 18.711 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.000 0.000 -87.241 │ │ SnC Astrocyte 63.523 396.028 4.404 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte 0.656 68.328 20.361 0.973 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ================================================== Covariate Condition[T.Space Flight] Cell Type Astrocyte SnC Astrocyte * ==========================================================================================================================
Differential Gene Expression¶
Mainly using edgeR
Pseudo-Bulk & Examine Covariates¶
In [ ]:
pdata = scflow.tl.create_pseudobulk(
self.rna, [i for i in [col_sample, col_condition, col_age] if i],
"Senescent_Cell_Label_by_Type", layer="counts", mode="sum")
pdata.layers["counts"] = pdata.X.copy()
sc.pp.normalize_total(pdata, target_sum=1e6)
sc.pp.log1p(pdata)
sc.pp.pca(pdata)
sc.pl.pca(pdata, color=pdata.obs, ncols=1, size=300)
Overall DEGs by Groups¶
Age¶
In older spaceflight mice compared to younger spaceflight mice
- 9630013a20Rik (CNS myelination and neural tissue regeneration) is downregulated.
- mt-Td is upregulated.
In [ ]:
%matplotlib inline
# Overall
print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
out_edgr_age = scflow.ax.run_deg_edgr(
self.rna, col_age, col_covariate=None, formula=None,
**keys[col_age], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title="Age DEGs: Overall Sample\n\n")
# Ground Control Subset
print(f"\n\n{'=' * 80}\nGround Control Subset\n{'=' * 80}\n\n")
out_edgr_age_gc = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_condition] == keys[
col_condition]["key_control"]],
col_age, col_covariate=None, formula=None,
**keys[col_age], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title="Age DEGs: Just Ground Control\n\n")
# Space Flight Subset
print(f"\n\n{'=' * 80}\nSpace Flight Subset\n{'=' * 80}\n\n")
out_edgr_age_sf = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_condition] == keys[
col_condition]["key_treatment"]],
col_age, col_covariate=None, formula=None, **keys[col_age],
log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype,
col_sample=col_sample, fig_title="Age DEGs: Just Space Flight\n\n")
Condition¶
out_edgr_con_o[0][(out_edgr_con_o[0].adj_p_value < 0.01) & (
out_edgr_con_o[0].abs_log_fc > 0.5)].sort_values(
"abs_log_fc", ascending=False)
Among the older mice, Mir6236 was differentially highly expressed in aged spaceflight mice compared to ground control mice (LFC=1.95, adjusted p=2.61e-13 in the overall sample; LFC=2.11, adjusted p=0.000015 in the aged subset). Suppression of this gene has been linked to neuronal morphogenesis (https://pubs.acs.org/doi/abs/10.1021/acsabm.0c01389) and therefore may represent a candidate intervention target.
Il31ra, which has been linked to neuroinflammation (https://advanced.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202409086), was also more highly expressed in spaceflight mice compared to ground control mice (LFC=1.52, adjusted p<0.001 in the overall sample; LFC=1.53, adjusted p<0.001 in the aged subset).
UBA52, also more highly expressed in spaceflight mice (LFC=1.43, adjusted p<0.001 in the overall sample; LFC=1.45, adjusted p<0.001 in the aged subset), may serve a neuroprotective role: It has been shown underexpressed in PD (https://www.mdpi.com/2073-4409/11/23/3770) and plays a role in neuroautophagy (https://www.tandfonline.com/doi/pdf/10.1080/15548627.2024.2395727). Likewise, Pomp, a gene associated with oxidative stress resistance and protein clearance [CITATION] was also more highly expressed in spaceflight mice. In contrast to past observations of downregulation of Uqcr11 in Alheimer's (https://www.benthamdirect.com/content/journals/car/10.2174/1567205014666170505095921), that gene was differentially highly expressed in spaceflight mice (LFC=1.23 aged subset; LFC=1.44 overall; p<0.001); however, Cmss1, which has been associated with Alzheimer's in APOE4 mice (https://pmc.ncbi.nlm.nih.gov/articles/PMC8064208/), was also more highly expressed in spaceflight mice.
Genes involved in oxidative phosphorylation and the electron transport chain, including Atp5e, Ndufa12, and Uqcr11 were also more highly expressed in spaceflight mice. Uqcr11 has shown an anti-apoptotic and oxidative stress alleviation role in mice (https://www.mdpi.com/2073-4425/16/5/526).
Tpt1 and Rn7sk were also upregulated in spaceflight mice. Tpt1, which is associated with cancer progression, anti-apoptosis, and cell stress response, has been shown to have a potentially bi-directional relationship with cell cycle control gene and canonical senescence marker TP53 (https://www.tandfonline.com/doi/pdf/10.4161/cc.25404). Rn7sk is associated with cellular senescence (https://onlinelibrary.wiley.com/doi/abs/10.1002/jcp.28119) and Alzheimer's (https://www.nature.com/articles/s41598-024-82490-9.pdf).
Pvalb overall sample (LFC=1.61, p=1.25e-11)
Trim17
Cck
Pam16
Mitochondrial/electron transport chain genes whose upregulation has been linked to Parkinson's disease (https://pmc.ncbi.nlm.nih.gov/articles/PMC10259399/pdf/AMS-19-3-131629.pdf) and/or adaptive responses to neurodegeneration in Alheimer's mouse models (https://journals.sagepub.com/doi/abs/10.1177/13872877251314847), including *mt-Atp6, mt-Co1, mt-Cytb mt-Nd2, and mt-Nd5, mt-Nd6...pregulation of *mt-Rnr1, which also showed higher expression in spaceflight aged mice, has been seen in response to ionizing radiation, possibly reflecting increased mitochonrial biogenesis as an adaptive stress response (https://pmc.ncbi.nlm.nih.gov/articles/PMC5555881)**
In [ ]:
# Overall
print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
out_edgr_con = scflow.ax.run_deg_edgr(
self.rna, col_condition, col_covariate=None, formula=None,
**keys[col_condition], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title=f"{col_condition} DEGs (Overall Sample)\n\n")
out_edgr_con_df = out_edgr_con[0][out_edgr_con[
0].adj_p_value < 0.001].sort_values("abs_log_fc", ascending=False)
goe = out_edgr_con_df.variable[:10].to_list()
print(out_edgr_con_df.iloc[:min(100, out_edgr_con_df.shape[0])], "\n\n", goe)
plt.show()
# Just Old
print(f"\n\n{'=' * 80}\nAged Subset\n{'=' * 80}\n\n")
out_edgr_con_o = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]],
col_condition, col_covariate=None, formula=None,
**keys[col_condition], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title=f"{col_condition} DEGs (Just Aged)\n\n")
plt.show()
out_edgr_con_o_df = out_edgr_con_o[0][out_edgr_con_o[
0].adj_p_value < 0.001].sort_values("abs_log_fc", ascending=False)
goes = out_edgr_con_o_df.variable[:10].to_list()
print(out_edgr_con_o_df, "\n\n", goes)
_ = self.plot(
kind=["violin", "matrix"], col_celltype=col_batch, layer="log1p",
violin=dict(col_wrap=5, hspace=0.25, rotation=45, figsize=(20, 20)),
matrix=dict(standard_scale="var"), genes=goes)
out_edgr_old_sf_v_gc = out_edgr_con_o_df[0][(
out_edgr_con_o_df[0].abs_log_fc > 1) & (
out_edgr_con_o_df[0].adj_p_value < 0.001)].sort_values(
"adj_p_value", ascending=True)
out_edgr_old_sf_v_gc = out_edgr_old_sf_v_gc.groupby("contrast").apply(
lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
:20]).set_index("variable", append=True).reset_index(
1, drop=True).rename_axis([col_condition, "variable"])
out_edgr_con_compare = out_edgr_con_o_df.set_index("variable")[[
"log_fc", "adj_p_value"]].join(out_edgr_con_df.set_index("variable")[[
"log_fc", "adj_p_value"]], how="inner", lsuffix="_Aged_Subset",
rsuffix="_Overall_Sample")
out_edgr_con_compare
Age x Condition¶
Spaceflight effect (main analysis), can visualize at different ages
Chchd2 (related to oxidative phosphorylation, cell migration, apoptosis inhibition, COX regulation, stress adaptation, Parkinson's, Lewy Body diseases, and cancer) was more highly expressed in spaceflight mice (and demonstrated a bigger young versus old difference in spaceflight mice). Spaceflight mice also showed higher expression of several ribosomal proteins (Rps16, Rpl31, Rpl17, Rpl9, Rpl15, Rps15a, Rpl19, Rps7, Rps23, Rpl37, Rps3a1, Rpl29, Rps15, Rps14).
Ndufa4 was also more highly expressed (implicated in cancer and mitochondrial dysfunction, part of the cytochrome c oxidase complex which drives oxidative phosphorylation, adaptation to hypoxia via downregulation of oxygen consumption and suppression of mitochondrial ROS production https://www.cell.com/cell-metabolism/fulltext/S1550-4131(11)00394-9?mobileUi=0) in spaceflight mice, as were other genes involved in ATP production/the electron transport chain and oxidative phosophorylation, including Cox7a2, Uqcr11, Ndufa12, Ndufa4, Ndufc1, Atp5o-1, Atp5e, and Uqcrh (which additionally has been implicated in tumor suppression https://aacrjournals.org/cancerres/article/80/16_Supplement/4795/643367).
In [ ]:
# With Single Label for Age x Condition
print(f"\n\n{'=' * 80}\nGroup (Single Factor)\n{'=' * 80}\n\n")
out_edgr_groups = scflow.ax.run_deg_edgr(
self.rna, col_batch, col_covariate=None, formula=None,
**keys[col_batch], col_sample=col_sample,
log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype)
print(list(out_edgr_groups[0].variable[:25]))
plt.show()
# _ = self.plot(kind="violin", col_celltype=col_batch,
# genes=list(out_edgr_groups[0].variable[:25]), layer="scaled",
# violin=dict(col_wrap=4, hspace=1, rotation=45))
out_edgr_batches = out_edgr_groups[0][(out_edgr_groups[0].abs_log_fc > 1) & (
out_edgr_groups[0].adj_p_value < 0.001)].sort_values(
"adj_p_value", ascending=True)
out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply(
lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
:20]).set_index("variable", append=True).reset_index(
1, drop=True).rename_axis([col_condition, "variable"])
# With Contrasts (Age & Condition = Separate Factors)
print(f"\n\n{'=' * 80}\nAge * Condition (2-Factor Contrast)\n{'=' * 80}\n\n")
out_edgr_contrasts = scflow.ax.run_deg_edgr(
self.rna, col_condition, col_covariate=col_age, formula=None,
**keys[col_condition], log2fc_thresh=0, n_top_vars=24,
col_celltype=col_celltype, col_sample=col_sample)
_ = self.plot(
kind=["violin", "matrix"], col_celltype=col_batch,
genes=out_edgr_contrasts[0][out_edgr_contrasts[
0].adj_p_value < 0.001].sort_values(
"abs_log_fc", ascending=False).variable[:10], layer="log1p",
violin=dict(col_wrap=5, hspace=0.25, rotation=45, figsize=(20, 20)),
matrix=dict(standard_scale="var"))
# Condition x Age
# Vulnerability of mice who were older at launch to spaceflight stress
out_edgr_contrasts_age = scflow.ax.run_deg_edgr(
self.rna, col_age, col_covariate=col_condition, formula=None,
**keys[col_age], log2fc_thresh=0, n_top_vars=24,
col_celltype=col_celltype, col_sample=col_sample)
Senescence Gene Contrasts Results¶
In [ ]:
# Genes
sen_genes_in_contrasts = list(set(genes).intersection(
out_edgr_contrasts[0].variable.unique())) # senescence genes in contrasts
out_edgr_snc = out_edgr_contrasts[0].set_index(
"variable").loc[sen_genes_in_contrasts] # contrast results for snc genes
res_snc_contrast = out_edgr_snc[out_edgr_snc.adj_p_value < 0.01] # p < 0.01
sig_contrast_genes = list(res_snc_contrast.index.values) # significant genes
# Plots By Groups; Axis by Cell Types
kws = dict(dot=dict(figsize=(10, 5)), heat=dict(figsize=(15, 15)))
_ = self.plot(kind=["matrix", "dot"], genes=sig_contrast_genes,
standard_scale="obs", col_celltype=col_celltype,
by_group=col_batch, **kws)
# Plots By Cell Types; Axis by Groups
# kws = dict(dot=dict(figsize=(10, 5)), heat=dict(figsize=(15, 15)),
# violin=dict(figsize=(30, 20), col_wrap=4, xlabel=None,
# rotation=45, hspace=1, top=0.95))
kws = dict(dot=dict(figsize=(10, 5)), heat=dict(figsize=(15, 15)))
_ = self.plot(kind=["matrix", "dot"], genes=sig_contrast_genes,
standard_scale="obs", col_celltype=col_batch,
by_group=col_celltype, **kws)
# Results
print(f"\n{'=' * 80}\nSignificant Senescence Gene Contrasts\n{'=' * 80}\n")
print(sig_contrast_genes)
res_snc_contrast
Snc versus Not (Group=Covariate)¶
In [ ]:
# Group = Covariate
out_edgr_contrasts_snc = scflow.ax.run_deg_edgr(
self.rna, "Senescent_Cell_Label",
col_covariate=col_batch, formula=None,
key_treatment="Senescent", key_control="Non-Senescent",
log2fc_thresh=0, n_top_vars=24, xlabel_rotation=45,
hspace=0.5, wspace=0.2, top=0.92, legend_loc="upper right",
col_celltype=col_celltype, col_sample=col_sample)
SnC versus Not SnC (Group=Subset)¶
In [ ]:
# Aged Subset
subset_old = self.rna.obs[col_age] == keys[col_age]["key_treatment"]
# Old Ground Control SnCs
print(f"\n\n{'=' * 80}\nGround Control (Aged)\n{'=' * 80}\n\n")
subset_gc = self.rna.obs[col_condition] == keys[
col_condition]["key_control"]
out_edgr_contrasts_snc_gc = scflow.ax.run_deg_edgr(
self.rna[subset_gc & subset_old], "Senescent_Cell_Label",
key_treatment="Senescent", top=0.92,
key_control="Non-Senescent", log2fc_thresh=0, n_top_vars=24,
xlabel_rotation=45, hspace=0.5, wspace=0.2, legend_loc="upper right",
col_celltype=col_celltype, col_sample=col_sample)
if len(self.rna[subset_gc & subset_old].obs[col_batch].unique()) > 1:
raise ValueError("Check subsetting")
key_1 = self.rna[subset_gc & subset_old].obs[col_batch].unique()[0]
# Old Spaceflight SnCs
print(f"\n\n{'=' * 80}\nSpaceflight (Aged)\n{'=' * 80}\n\n")
subset_sf = self.rna.obs[col_condition] == keys[
col_condition]["key_treatment"]
out_edgr_contrasts_snc_sf = scflow.ax.run_deg_edgr(
self.rna[subset_sf & subset_old], "Senescent_Cell_Label",
key_treatment="Senescent", key_control="Non-Senescent",
log2fc_thresh=0, n_top_vars=24, xlabel_rotation=45,
hspace=0.5, wspace=0.2, top=0.92, legend_loc="upper right",
col_celltype=col_celltype, col_sample=col_sample)
if len(self.rna[subset_sf & subset_old].obs[col_batch].unique()) > 1:
raise ValueError("Check subsetting")
key_2 = self.rna[subset_sf & subset_old].obs[col_batch].unique()[0]
# Compare
res_snc_gc = out_edgr_contrasts_snc_gc[0][out_edgr_contrasts_snc_gc[
0].adj_p_value < 0.001].sort_values("adj_p_value").set_index("variable")
res_snc_sf = out_edgr_contrasts_snc_sf[0][out_edgr_contrasts_snc_sf[
0].adj_p_value < 0.001].sort_values("adj_p_value").set_index("variable")
res_snc_grps = pd.concat([
res_snc_gc, res_snc_sf], keys=[key_1, key_2], names=[col_condition])
res_snc_grps = res_snc_grps[res_snc_grps.abs_log_fc >= 1]
# top_tx_g_snc = res_snc_grps.loc[keys[col_condition][
# "key_treatment"]].sort_values("abs_log_fc", ascending=False).index
# print(res_snc_grps.unstack(0).replace(np.nan, "")[[
# "log_fc", "adj_p_value"]].loc[top_tx_g_snc])
# just_sf = res_snc_grps.loc[keys[col_condition][
# "key_treatment"]].index.difference(res_snc_grps.loc[keys[
# col_condition]["key_control"]].index)
# just_gc = res_snc_grps.loc[keys[col_condition][
# "key_control"]].index.difference(res_snc_grps.loc[keys[
# col_condition]["key_treatment"]].index)
# print(res_snc_grps.loc[:, just_gc, :].sort_values(
# "abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]], "\n\n")
# res_snc_grps.loc[:, just_sf, :].sort_values(
# "abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]]
res_snc_grps.unstack(0).replace(np.nan, "")[["log_fc", "adj_p_value"]]
Group DEGs (SnC=Covariate)¶
In [ ]:
out_edgr_contrasts_snc_cov = {}
for a in self.rna.obs[col_age].unique():
print(f"\n\n{'=' * 80}\nAge = {a}\n{'=' * 80}\n\n")
out_edgr_contrasts_snc_cov[a] = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_age] == a], col_condition,
col_covariate="Senescent_Cell_Label",
**keys[col_condition], log2fc_thresh=0, n_top_vars=24,
xlabel_rotation=45, hspace=0.5, wspace=0.2, top=0.85,
legend_loc="upper right", col_celltype=col_celltype,
col_sample=col_sample)
plt.show()
Group DEGs (SnC=Subset)¶
All Groups SnCs¶
In [ ]:
out_edgr_groups_snc = scflow.ax.run_deg_edgr(
self.rna[(self.rna.obs["Senescent_Cell_Label"] == "Senescent")],
col_batch, **keys[col_batch], col_sample=col_sample,
log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype)
print(list(out_edgr_groups[0].variable[:25]))
out_edgr_batches_snc = out_edgr_groups[0][(
out_edgr_groups[0].abs_log_fc > 1) & (
out_edgr_groups[0].adj_p_value < 0.001)].sort_values(
"adj_p_value", ascending=True)
out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply(
lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
:20]).set_index("variable", append=True).reset_index(
1, drop=True).rename_axis([col_condition, "variable"])
plt.show()
Just Aged SnCs¶
In [ ]:
out_edgr_contrasts_sncto, snc_degs_by_condition_age_ct = {}, {}
thresh_p_c, thresh_lfcabs_c = 0.05, 0.5 # thresholds
subs_old = self.rna.obs[col_age] == keys[col_age]["key_treatment"]
subs_sf = self.rna.obs[col_condition] == keys[col_condition]["key_treatment"]
# subs_ac = (self.rna.obs["Senescent_Cell_Label"] == "Senescent") & (
# subs_old | subs_sf)
subs_ac = (self.rna.obs["Senescent_Cell_Label"] == "Senescent") & (subs_old)
out_edgr_contrasts_sncto["Overall"] = scflow.ax.run_deg_edgr(
self.rna[subs_ac], col_condition,
# col_covariate=col_age,
**keys[col_condition],
log2fc_thresh=0, n_top_vars=24,
legend_loc="upper center", fig_title=f"Overall",
wspace=0.25, bottom=0.3, hspace=1 if len(x) > 10 else 0.4, top=0.95,
kws_xticks=dict(fontsize=10, rotation=45 if len(x) > 10 else None),
col_celltype=col_celltype, col_sample=col_sample)
plt.show()
tmp = out_edgr_contrasts_sncto["Overall"][0]
tmp = tmp[(tmp.adj_p_value < thresh_p_c) & (
tmp.abs_log_fc >= thresh_lfcabs_c)] # filter by p & lfc
snc_degs_by_condition_age_ct["Overall"] = list(
tmp.sort_values("adj_p_value", ascending=True).variable)
for x in self.rna.obs[col_celltype].unique():
print(x)
out_edgr_contrasts_sncto[x] = scflow.ax.run_deg_edgr(
self.rna[(subs_ac) & (self.rna.obs[col_celltype] == x)],
col_condition,
# col_covariate=col_age,
**keys[col_condition],
log2fc_thresh=0, n_top_vars=24,
legend_loc="upper center", fig_title=f"Senescent {x}",
wspace=0.25, bottom=0.3, hspace=1 if len(x) > 10 else 0.4, top=0.95,
kws_xticks=dict(fontsize=10, rotation=45 if len(x) > 10 else None),
col_celltype=col_celltype, col_sample=col_sample)
plt.show()
tmp = out_edgr_contrasts_sncto[x][0]
tmp = tmp[(tmp.adj_p_value < thresh_p_c) & (
tmp.abs_log_fc >= thresh_lfcabs_c)] # filter by p & lfc
snc_degs_by_condition_age_ct[x] = list(tmp.sort_values(
"adj_p_value", ascending=True).variable)
del tmp
out_edgr_contrasts_snc_all_top = pd.concat([out_edgr_contrasts_sncto[x][
0].set_index("variable").loc[snc_degs_by_condition_age_ct[
x]] for x in out_edgr_contrasts_sncto], keys=out_edgr_contrasts_sncto,
names=[col_celltype])
out_edgr_contrasts_snc_all = pd.concat([
out_edgr_contrasts_sncto[x][0].set_index("variable")
for x in out_edgr_contrasts_sncto], keys=out_edgr_contrasts_sncto,
names=[col_celltype])
out_edgr_contrasts_snc_all_top
More Specific Contrasts (pyDESeq2)¶
Group¶
In [ ]:
# pdata = scflow.tl.create_pseudobulk(
# self.rna, [col_sample, col_condition, col_age],
# col_celltype, layer="counts", mode="sum")
# design = f"~{col_condition} + {col_age} + {col_condition} * {col_age}"
# # edgr = pt.tl.EdgeR(pdata, design=design)
# pds2 = pt.tl.PyDESeq2(adata=pdata, design=design)
# pds2.fit()
# key_control, key_treatment = [keys[col_condition][k] for k in [
# "key_control", "key_treatment"]]
# key_control_cov, key_treatment_cov = [keys[col_age][k] for k in [
# "key_control", "key_treatment"]]
# sf_old = {col_condition: key_treatment, col_age: key_treatment_cov}
# sf_yng = {col_condition: key_treatment, col_age: key_control_cov}
# gc_old = {col_condition: key_control, col_age: key_treatment_cov}
# gc_yng = {col_condition: key_control, col_age: key_control_cov}
# # Spaceflight-Specific Age DEGs
# spaceflight_specfic_age_degs = (pds2.cond(**sf_old) - pds2.cond(
# **sf_yng)) - (pds2.cond(**gc_old) - pds2.cond(**gc_yng))
# interaction_res_df = pds2.test_contrasts(spaceflight_specfic_age_degs)
# pds2.plot_volcano(interaction_res_df, log2fc_thresh=0)
# # Age-Specific Spaceflight DEGs
# age_specfic_sf_degs = (pds2.cond(**sf_old) - pds2.cond(**gc_old)) - (
# pds2.cond(**sf_yng) - pds2.cond(**gc_yng))
# interaction_res_df2 = pds2.test_contrasts(age_specfic_sf_degs)
# pds2.plot_volcano(interaction_res_df2, log2fc_thresh=0)
# # Old vs. Young Spaceflight
# age_sf_ix = pds2.cond(**sf_old) - pds2.cond(**sf_yng)
# interaction_res_df3 = pds2.test_contrasts(age_sf_ix)
# pds2.plot_volcano(interaction_res_df3, log2fc_thresh=0)
# # Old vs. Young GC
# age_gc_ix = pds2.cond(**gc_old) - pds2.cond(**gc_yng)
# interaction_res_df4 = pds2.test_contrasts(age_gc_ix)
# pds2.plot_volcano(interaction_res_df4, log2fc_thresh=0)
# # SF vs. GC Old
# cond_old_ix = pds2.cond(**sf_old) - pds2.cond(**gc_old)
# interaction_res_df5 = pds2.test_contrasts(cond_old_ix)
# pds2.plot_volcano(interaction_res_df5, log2fc_thresh=0)
# # SF vs. GC Young
# cond_yng_ix = pds2.cond(**sf_yng) - pds2.cond(**gc_yng)
# interaction_res_df6 = pds2.test_contrasts(cond_yng_ix)
# pds2.plot_volcano(interaction_res_df6, log2fc_thresh=0)
# # Compare
# thresh_lfc, thresh_p = 1, 0.001
# sf_deg_res_df_old = interaction_res_df5[(
# interaction_res_df5.adj_p_value < thresh_p) & (
# interaction_res_df5.log_fc.abs() >= thresh_lfc)].set_index(
# "variable")
# sf_deg_res_df_yng = interaction_res_df6[(
# interaction_res_df6.adj_p_value < thresh_p) & (
# interaction_res_df6.log_fc.abs() >= thresh_lfc)].set_index(
# "variable")
# sf_deg_res = pd.concat([sf_deg_res_df_old, sf_deg_res_df_yng],
# keys=["Old", "Young"], names=["Age Group"])
# sf_deg_res.loc[:, "log_fc_abs"] = sf_deg_res.log_fc.abs()
# sf_deg_res = sf_deg_res.sort_values("log_fc_abs", ascending=False)[[
# "log_fc", "adj_p_value"]].unstack(0)
# print("SF DEGs in Both Young & Old: ", sf_deg_res.dropna().index.values)
# print("\n\nSF DEGs in Just Young", list(sf_deg_res_df_yng.index.difference(
# sf_deg_res_df_old.index)), "\n\n")
# print("SF DEGs in Just Old", list(sf_deg_res_df_old.index.difference(
# sf_deg_res_df_yng.index)), "\n\n")
# sf_deg_res.replace(np.nan, "")
# # Marker Set Overlap
# print("Both: ", marker_gene_sets.apply(
# lambda i: set(sf_deg_res.dropna().index).intersection(set(i))).apply(
# lambda x: x if len(x) > 0 else np.nan).dropna(), "\n\n")
# print("Young: ", marker_gene_sets.apply(
# lambda i: sf_deg_res_df_yng.index.difference(
# sf_deg_res_df_old.index).intersection(set(i))).apply(
# lambda x: x if len(x) > 0 else np.nan).dropna(), "\n\n")
# print("Old: ", marker_gene_sets.apply(
# lambda i: sf_deg_res_df_old.index.difference(
# sf_deg_res_df_yng.index).intersection(set(i))).apply(
# lambda x: x if len(x) > 0 else np.nan).dropna(), "\n\n")
SnCs¶
Old¶
In [ ]:
# Setup
design = (f"~{col_condition} + Senescent_Cell_Label + "
f"{col_condition} * Senescent_Cell_Label")
pdata2 = scflow.tl.create_pseudobulk(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]], [
col_sample, col_condition, "Senescent_Cell_Label"],
"Senescent_Cell_Label_by_Type", layer="counts", mode="sum")
pds2_snc_old = pt.tl.PyDESeq2(adata=pdata2, design=design)
pds2_snc_old.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = "Non-Senescent", "Senescent"
sf_sc = {col_condition: key_treatment,
"Senescent_Cell_Label": key_treatment_cov}
sf_ns = {col_condition: key_treatment,
"Senescent_Cell_Label": key_control_cov}
gc_snc = {col_condition: key_control,
"Senescent_Cell_Label": key_treatment_cov}
gc_ns = {col_condition: key_control, "Senescent_Cell_Label": key_control_cov}
# SnC DEGs
res_df_snc = pds2_snc_old.test_contrasts(pds2_snc_old.contrast(
column="Senescent_Cell_Label", baseline=key_control_cov,
group_to_compare=key_treatment_cov))
print(res_df_snc[res_df_snc.adj_p_value < 0.001].head(10))
pds2_snc_old.plot_volcano(res_df_snc, log2fc_thresh=0)
# Spaceflight-Specific SnC DEGs
spaceflight_specfic_snc_degs = (pds2_snc_old.cond(
**sf_sc) - pds2_snc_old.cond(**sf_ns)) - (
pds2_snc_old.cond(**gc_snc) - pds2_snc_old.cond(**gc_ns))
interaction_res_df_snc_sf = pds2_snc_old.test_contrasts(
spaceflight_specfic_snc_degs)
print(interaction_res_df_snc_sf[
interaction_res_df_snc_sf.adj_p_value < 0.001].head(10))
pds2_snc_old.plot_volcano(interaction_res_df_snc_sf, log2fc_thresh=0)
Young¶
In [ ]:
# Setup
design = (f"~{col_condition} + Senescent_Cell_Label + "
f"{col_condition} * Senescent_Cell_Label")
pdata2 = scflow.tl.create_pseudobulk(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]], [
col_sample, col_condition, "Senescent_Cell_Label"],
"Senescent_Cell_Label_by_Type", layer="counts", mode="sum")
pds2_snc_yng = pt.tl.PyDESeq2(adata=pdata2, design=design)
pds2_snc_yng.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = "Non-Senescent", "Senescent"
sf_sc = {col_condition: key_treatment,
"Senescent_Cell_Label": key_treatment_cov}
sf_ns = {col_condition: key_treatment,
"Senescent_Cell_Label": key_control_cov}
gc_snc = {col_condition: key_control,
"Senescent_Cell_Label": key_treatment_cov}
gc_ns = {col_condition: key_control, "Senescent_Cell_Label": key_control_cov}
# SnC DEGs
res_df_snc = pds2_snc_yng.test_contrasts(pds2_snc_yng.contrast(
column="Senescent_Cell_Label", baseline=key_control_cov,
group_to_compare=key_treatment_cov))
print(res_df_snc[res_df_snc.adj_p_value < 0.001].head(10))
pds2_snc_yng.plot_volcano(res_df_snc, log2fc_thresh=0)
# Spaceflight-Specific SnC DEGs
spaceflight_specfic_snc_degs = (pds2_snc_yng.cond(
**sf_sc) - pds2_snc_yng.cond(**sf_ns)) - (
pds2_snc_yng.cond(**gc_snc) - pds2_snc_yng.cond(**gc_ns))
interaction_res_df_snc_sf = pds2_snc_yng.test_contrasts(
spaceflight_specfic_snc_degs)
pds2_snc_yng.plot_volcano(interaction_res_df_snc_sf, log2fc_thresh=0)
SnCs (More Permissive Threshold)¶
In [ ]:
# Compare Less & More Permissive Thresholds
print("Less & More Permissive SnC Threshold: ", self.rna.obs[[
"Senescent_Cell_Label", f"Senescent_Cell_Label_{use_metric}_{p_h}"
]].value_counts())
sen_lab = f"Senescent_Cell_Label_{use_metric}_{p_h}"
# Setup
design = (f"~{col_condition} + {sen_lab} + "
f"{col_condition} * {sen_lab}")
pdata2_3 = scflow.tl.create_pseudobulk(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]], [
col_sample, col_condition, sen_lab],
col_celltype, layer="counts", mode="sum")
pds2_snc_old_3 = pt.tl.PyDESeq2(adata=pdata2_3, design=design)
pds2_snc_old_3.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = "Non-Senescent", "Senescent"
sf_sc = {col_condition: key_treatment, sen_lab: key_treatment_cov}
sf_ns = {col_condition: key_treatment, sen_lab: key_control_cov}
gc_snc = {col_condition: key_control, sen_lab: key_treatment_cov}
gc_ns = {col_condition: key_control, sen_lab: key_control_cov}
# SnC DEGs
res_df_snc_3 = pds2_snc_old_3.test_contrasts(pds2_snc_old_3.contrast(
column=sen_lab, baseline=key_control_cov,
group_to_compare=key_treatment_cov))
print(res_df_snc_3[res_df_snc_3.adj_p_value < 0.001].head(10))
pds2_snc_old_3.plot_volcano(res_df_snc_3, log2fc_thresh=0)
# Spaceflight-Specific SnC DEGs
spaceflight_specfic_snc_degs_3 = (pds2_snc_old_3.cond(
**sf_sc) - pds2_snc_old_3.cond(**sf_ns)) - (
pds2_snc_old_3.cond(**gc_snc) - pds2_snc_old_3.cond(**gc_ns))
interaction_res_df_snc_sf_3 = pds2_snc_old_3.test_contrasts(
spaceflight_specfic_snc_degs_3)
pds2_snc_old_3.plot_volcano(interaction_res_df_snc_sf_3, log2fc_thresh=0)
MAST¶
In [ ]:
# %%R
# find_de_MAST_RE <- function(adata_, formula){
# # create a MAST object
# sca <- SceToSingleCellAssay(adata_, class="SingleCellAssay")
# print("Dimensions before subsetting:")
# print(dim(sca))
# print("")
# # keep genes that are expressed in more than 10% of all cells
# sca <- sca[freq(sca) > 0.1,]
# print("Dimensions after subsetting:")
# print(dim(sca), "\n")
# cdr2 <- colSums(assay(sca) > 0)
# colData(sca)$ngeneson <- scale(cdr2)
# # store the columns that we are interested in as factors
# label <- factor(colData(sca)$label)
# # set the reference level
# label <- relevel(label,"ctrl")
# colData(sca)$label <- label
# celltype <- factor(colData(sca)$cell_type)
# colData(sca)$celltype <- celltype
# # same for donors (which we need to model random effects)
# replicate <- factor(colData(sca)$replicate)
# colData(sca)$replicate <- replicate
# # create a group per condition-celltype combination
# colData(sca)$group <- paste0(colData(
# adata_)$label, ".", colData(adata_)$cell_type)
# colData(sca)$group <- factor(colData(sca)$group)
# # define and fit the model
# zlmCond <- zlm(formula=formula,
# sca=sca,
# method="glmer",
# ebayes=F,
# strictConvergence=F,
# fitArgsD=list(nAGQ = 0)) # to speed up calculations
# # perform likelihood-ratio test for the condition
# summaryCond <- summary(zlmCond, doLRT="groupstim.CD14_Monocytes")
# # get the table with log-fold changes and p-values
# summaryDt <- summaryCond$datatable
# return(summaryDt)
# }
SnC GEX Profiles¶
Pathway Analysis¶
In [ ]:
names = gp.get_library_name()
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
"MSigDB_Hallmark_2020", # "DGIdb_Drug_Targets_2024",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reimport pdb;actome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
gene_sets = dict(zip(["up", "down"], [[
f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
for i in ["up", "down"]]))
def fx_replace_string(string):
"""Make short version of pathway terms."""
string = re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?", "\\1 v \\2 mo.", re.sub(
"SRP[0-9]+ ", "", re.sub("GSE[0-9]+ ", "", string))))
string = re.sub(".*.xlsx.", "", re.sub(
".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub("([0-9]+) years v ([0-9]+) years",
"\\1 v \\2 yrs.", string))))
string = re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "", re.sub(
".*Supplementary Table [0-9]+[-]?", "", string))))
return string
Endrichr¶
In [ ]:
out_pathway_snc_edgr_contrasts = scflow.ax.run_enrichr(
out_edgr_contrasts_snc_all_top, col_grouping=["Comparison", col_celltype],
gene_sets=gene_sets, fx_replace_string=fx_replace_string, title="Overall")
SnC vs. Not¶
In [ ]:
# res_dc_ulm = scflow.ax.run_decoupler_ulm(
# self.rna[self.rna.obs["Senescent_Cell_Label"] == "Senescent"],
# # "Senescent_Cell_Label_by_Type",
# # "Senescent_Cell_Label",
# col_condition, col_condition="Senescent_Cell_Label_by_Type",
# species=species, resource="hallmark",
# top=0.95, hspace=1, wspace=0.5, figsize=(15, 10))
Pathway Set = Progeny¶
In [ ]:
res_dc_ulm = scflow.ax.run_decoupler_ulm(
self.rna[self.rna.obs["Senescent_Cell_Label"] == "Senescent"],
col_condition, col_condition="Senescent_Cell_Label_by_Type",
species=species, resource="progeny",
hspace=0.5, wspace=0.5, top=0.95, figsize=(15, 15))
res_dc_ulm_scores = pd.concat([res_dc_ulm[0][x][1].set_index(
"group").rename_axis(col_condition).set_index(
"name", append=True) for x in res_dc_ulm[0]],
keys=res_dc_ulm[0], names=[col_celltype])
res_dc_ulm_pathways = pd.concat([pd.Series(res_dc_ulm[0][x][2])
for x in res_dc_ulm[0]], keys=res_dc_ulm[0])
print(res_dc_ulm_pathways)
res_dc_ulm_scores
reduce expected at least 2 arguments, got 1
SnC OPC Ground Control [Androgen, Estrogen, VEGF]
Space Flight [NFkB, PI3K, TNFa]
SnC Neuron Ground Control [Estrogen, Androgen, NFkB]
Space Flight [PI3K, VEGF, EGFR]
SnC Oligodendrocyte Ground Control [Estrogen, Hypoxia, WNT]
Space Flight [PI3K, EGFR, Trail]
SnC Endothelial Ground Control [TGFb, VEGF, Androgen]
Space Flight [Trail, Estrogen, EGFR]
SnC Microglial Ground Control [Estrogen, Hypoxia, Androgen]
Space Flight [JAK-STAT, Trail, TNFa]
SnC Astrocyte Ground Control [Estrogen, MAPK, JAK-STAT]
Space Flight [PI3K, VEGF, Hypoxia]
dtype: object
Out[ ]:
| reference | stat | meanchange | pval | padj | |||
|---|---|---|---|---|---|---|---|
| annotation_scanvi_collapsed | Condition | name | |||||
| SnC OPC | Ground Control | Androgen | rest | 2.757708 | 0.724846 | 8.382574e-03 | 1.173560e-01 |
| Estrogen | rest | 2.081309 | 0.557417 | 4.317564e-02 | 1.511148e-01 | ||
| VEGF | rest | 1.899823 | 0.569651 | 6.389319e-02 | 1.676413e-01 | ||
| MAPK | rest | 0.605538 | 0.177445 | 5.480702e-01 | 6.244029e-01 | ||
| JAK-STAT | rest | 0.136164 | 0.043412 | 8.922867e-01 | 8.922867e-01 | ||
| Space Flight | NFkB | rest | 2.248224 | 0.778948 | 3.000980e-02 | 1.511148e-01 | |
| PI3K | rest | 2.095528 | 0.545715 | 4.166130e-02 | 1.511148e-01 | ||
| TNFa | rest | 1.844120 | 0.579368 | 7.184625e-02 | 1.676413e-01 | ||
| EGFR | rest | 1.479733 | 0.627585 | 1.457831e-01 | 2.915662e-01 | ||
| Trail | rest | 1.272377 | 0.368791 | 2.098306e-01 | 3.672036e-01 | ||
| TGFb | rest | 1.078802 | 0.391425 | 2.866111e-01 | 4.458395e-01 | ||
| WNT | rest | 0.931677 | 0.224857 | 3.565740e-01 | 4.992036e-01 | ||
| p53 | rest | 0.724510 | 0.293513 | 4.724453e-01 | 6.012941e-01 | ||
| Hypoxia | rest | 0.558034 | 0.205651 | 5.798027e-01 | 6.244029e-01 | ||
| SnC Neuron | Ground Control | Estrogen | rest | 9.500359 | 0.482193 | 6.141583e-21 | 8.598216e-20 |
| Androgen | rest | 7.283937 | 0.341144 | 4.741372e-13 | 1.659480e-12 | ||
| NFkB | rest | 5.826446 | 0.328417 | 6.703352e-09 | 1.876939e-08 | ||
| MAPK | rest | 5.476003 | 0.360809 | 4.954974e-08 | 1.156161e-07 | ||
| JAK-STAT | rest | 3.427728 | 0.188864 | 6.223002e-04 | 1.244600e-03 | ||
| TNFa | rest | 0.905685 | 0.049524 | 3.652234e-01 | 4.260939e-01 | ||
| Trail | rest | 0.356124 | 0.013530 | 7.217877e-01 | 7.773099e-01 | ||
| WNT | rest | 0.032851 | 0.001281 | 9.737973e-01 | 9.737973e-01 | ||
| Space Flight | PI3K | rest | 6.765508 | 0.479925 | 2.158089e-11 | 1.510663e-10 | |
| VEGF | rest | 6.238393 | 0.465892 | 6.091033e-10 | 2.842482e-09 | ||
| EGFR | rest | 2.727422 | 0.276192 | 6.475309e-03 | 1.133179e-02 | ||
| Hypoxia | rest | 1.675481 | 0.124868 | 9.409898e-02 | 1.463762e-01 | ||
| TGFb | rest | 1.351366 | 0.101788 | 1.768255e-01 | 2.475557e-01 | ||
| p53 | rest | 0.896480 | 0.073842 | 3.701778e-01 | 4.711353e-01 | ||
| SnC Oligodendrocyte | Ground Control | Estrogen | rest | 3.334559 | 0.414669 | 1.005425e-03 | 1.407595e-02 |
| Hypoxia | rest | 2.820404 | 0.404899 | 5.249221e-03 | 3.674455e-02 | ||
| WNT | rest | 1.900624 | 0.212298 | 5.868023e-02 | 2.033951e-01 | ||
| MAPK | rest | 1.777636 | 0.269305 | 7.692159e-02 | 2.033951e-01 | ||
| TGFb | rest | 1.718262 | 0.195343 | 8.716934e-02 | 2.033951e-01 | ||
| NFkB | rest | 0.808573 | 0.100958 | 4.196512e-01 | 8.032411e-01 | ||
| VEGF | rest | 0.458603 | 0.058390 | 6.469758e-01 | 8.032411e-01 | ||
| Space Flight | PI3K | rest | 1.959169 | 0.238152 | 5.119465e-02 | 1.515907e-01 | |
| EGFR | rest | 0.761928 | 0.119689 | 4.468132e-01 | 7.819231e-01 | ||
| Trail | rest | 0.581383 | 0.057859 | 5.614986e-01 | 7.880744e-01 | ||
| p53 | rest | 0.464857 | 0.059114 | 6.424335e-01 | 7.880744e-01 | ||
| JAK-STAT | rest | 0.303798 | 0.034887 | 7.615324e-01 | 7.880744e-01 | ||
| Androgen | rest | 0.296035 | 0.040314 | 7.674461e-01 | 7.880744e-01 | ||
| TNFa | rest | 0.269097 | 0.032517 | 7.880744e-01 | 7.880744e-01 | ||
| SnC Endothelial | Ground Control | TGFb | rest | 1.400034 | 0.761347 | 1.779404e-01 | 3.417916e-01 |
| VEGF | rest | 0.978723 | 0.581423 | 3.453250e-01 | 5.371722e-01 | ||
| Androgen | rest | 0.786702 | 0.185250 | 4.404327e-01 | 6.166058e-01 | ||
| PI3K | rest | 0.627001 | 0.470541 | 5.371859e-01 | 6.836912e-01 | ||
| Hypoxia | rest | 0.335466 | 0.130014 | 7.404797e-01 | 7.974397e-01 | ||
| TNFa | rest | 0.016855 | 0.009220 | 9.867047e-01 | 9.867047e-01 | ||
| Space Flight | Trail | rest | 3.251158 | 2.080820 | 3.940980e-02 | 5.517372e-01 | |
| Estrogen | rest | 1.942157 | 1.748921 | 1.574474e-01 | 9.423082e-01 | ||
| EGFR | rest | 1.119047 | 1.727110 | 3.365294e-01 | 9.423082e-01 | ||
| WNT | rest | 1.008216 | 0.614008 | 3.934548e-01 | 9.423082e-01 | ||
| MAPK | rest | 0.865796 | 1.573822 | 4.555649e-01 | 9.423082e-01 | ||
| p53 | rest | 0.754090 | 0.666123 | 5.286061e-01 | 9.423082e-01 | ||
| NFkB | rest | 0.669465 | 0.632085 | 5.425169e-01 | 9.423082e-01 | ||
| JAK-STAT | rest | 0.178556 | 0.141105 | 8.690182e-01 | 9.423082e-01 | ||
| SnC Microglial | Ground Control | Estrogen | rest | 2.199313 | 0.604043 | 3.200895e-02 | 4.481254e-01 |
| Hypoxia | rest | 1.633230 | 0.419244 | 1.082607e-01 | 5.620789e-01 | ||
| Androgen | rest | 1.577148 | 0.417044 | 1.204455e-01 | 5.620789e-01 | ||
| TGFb | rest | 0.956054 | 0.250233 | 3.431805e-01 | 7.537423e-01 | ||
| MAPK | rest | 0.890919 | 0.285940 | 3.768712e-01 | 7.537423e-01 | ||
| VEGF | rest | 0.320028 | 0.068197 | 7.501809e-01 | 9.437506e-01 | ||
| Space Flight | JAK-STAT | rest | 1.393819 | 0.313861 | 1.680881e-01 | 5.883084e-01 | |
| Trail | rest | 1.119993 | 0.245419 | 2.667810e-01 | 6.766235e-01 | ||
| TNFa | rest | 0.535728 | 0.138733 | 5.941373e-01 | 9.390585e-01 | ||
| PI3K | rest | 0.279185 | 0.075402 | 7.809866e-01 | 9.390585e-01 | ||
| p53 | rest | 0.265101 | 0.066740 | 7.917737e-01 | 9.390585e-01 | ||
| WNT | rest | 0.228122 | 0.061728 | 8.202632e-01 | 9.390585e-01 | ||
| NFkB | rest | 0.126241 | 0.031720 | 8.999499e-01 | 9.390585e-01 | ||
| EGFR | rest | 0.076769 | 0.023985 | 9.390585e-01 | 9.390585e-01 | ||
| SnC Astrocyte | Ground Control | Estrogen | rest | 1.837555 | 0.285077 | 6.884086e-02 | 2.729383e-01 |
| MAPK | rest | 1.428339 | 0.303562 | 1.559084e-01 | 4.357931e-01 | ||
| JAK-STAT | rest | 0.018456 | 0.002780 | 9.853065e-01 | 9.853065e-01 | ||
| Space Flight | PI3K | rest | 2.631447 | 0.415732 | 9.604981e-03 | 1.344697e-01 | |
| VEGF | rest | 2.258330 | 0.426227 | 2.570189e-02 | 1.799132e-01 | ||
| Hypoxia | rest | 1.822952 | 0.373012 | 7.076687e-02 | 2.476841e-01 | ||
| Androgen | rest | 1.361468 | 0.281741 | 1.758971e-01 | 4.104266e-01 | ||
| EGFR | rest | 1.065640 | 0.216080 | 2.888035e-01 | 5.776071e-01 | ||
| p53 | rest | 0.839553 | 0.192130 | 4.028111e-01 | 7.049193e-01 | ||
| TGFb | rest | 0.654916 | 0.155232 | 5.137672e-01 | 7.991934e-01 | ||
| Trail | rest | 0.342109 | 0.050880 | 7.328643e-01 | 9.399009e-01 | ||
| WNT | rest | 0.316784 | 0.047958 | 7.519635e-01 | 9.399009e-01 | ||
| NFkB | rest | 0.246608 | 0.044382 | 8.056294e-01 | 9.399009e-01 | ||
| TNFa | rest | 0.049402 | 0.008985 | 9.606799e-01 | 9.849361e-01 |
Pathway Set = Hallmark¶
In [177]:
res_dc_ulm_hm = scflow.ax.run_decoupler_ulm(
self.rna[self.rna.obs["Senescent_Cell_Label"] == "Senescent"],
col_condition, col_condition="Senescent_Cell_Label_by_Type",
species=species, resource="hallmark",
hspace=0.5, top=0.95, figsize=(15, 15))
res_dc_ulm_hm_scores = pd.concat([res_dc_ulm_hm[0][x][1].set_index(
"group").rename_axis(col_condition).set_index(
"name", append=True) for x in res_dc_ulm_hm[0]],
keys=res_dc_ulm_hm[0], names=[col_celltype])
res_dc_ulm_hm_scores
Out[177]:
| reference | stat | meanchange | pval | padj | |||
|---|---|---|---|---|---|---|---|
| annotation_scanvi_collapsed | Condition | name | |||||
| SnC OPC | Ground Control | UNFOLDED_PROTEIN_RESPONSE | rest | 3.148356 | 0.573193 | 3.024018e-03 | 2.520015e-02 |
| APICAL_SURFACE | rest | 1.860512 | 0.476689 | 6.923833e-02 | 2.663013e-01 | ||
| HEME_METABOLISM | rest | 1.675026 | 0.389181 | 1.007165e-01 | 3.043063e-01 | ||
| ESTROGEN_RESPONSE_EARLY | rest | 1.603981 | 0.395632 | 1.156364e-01 | 3.043063e-01 | ||
| BILE_ACID_METABOLISM | rest | 1.409833 | 0.305282 | 1.653179e-01 | 3.757224e-01 | ||
| PROTEIN_SECRETION | rest | 1.291372 | 0.465757 | 2.046302e-01 | 3.818708e-01 | ||
| ESTROGEN_RESPONSE_LATE | rest | 1.000520 | 0.274508 | 3.225932e-01 | 5.376553e-01 | ||
| MITOTIC_SPINDLE | rest | 0.843344 | 0.305709 | 4.034980e-01 | 6.304656e-01 | ||
| DNA_REPAIR | rest | 0.779627 | 0.166768 | 4.396200e-01 | 6.554873e-01 | ||
| ANDROGEN_RESPONSE | rest | 0.747198 | 0.218936 | 4.588411e-01 | 6.554873e-01 | ||
| MYC_TARGETS_V2 | rest | 0.658130 | 0.143188 | 5.137388e-01 | 7.135262e-01 | ||
| PEROXISOME | rest | 0.619365 | 0.167230 | 5.387650e-01 | 7.280608e-01 | ||
| WNT_BETA_CATENIN_SIGNALING | rest | 0.227567 | 0.057822 | 8.209931e-01 | 9.331232e-01 | ||
| APOPTOSIS | rest | 0.203285 | 0.045779 | 8.398109e-01 | 9.331232e-01 | ||
| SPERMATOGENESIS | rest | 0.151917 | 0.043679 | 8.799167e-01 | 9.360816e-01 | ||
| INTERFERON_GAMMA_RESPONSE | rest | 0.060790 | 0.012859 | 9.517954e-01 | 9.914535e-01 | ||
| Space Flight | COAGULATION | rest | 4.250807 | 1.224458 | 1.137765e-04 | 5.688826e-03 | |
| ADIPOGENESIS | rest | 3.642291 | 0.960716 | 6.879389e-04 | 1.719847e-02 | ||
| APICAL_JUNCTION | rest | 3.270742 | 1.186918 | 2.297720e-03 | 2.520015e-02 | ||
| MYOGENESIS | rest | 3.192599 | 0.861542 | 2.743790e-03 | 2.520015e-02 | ||
| OXIDATIVE_PHOSPHORYLATION | rest | 3.166107 | 1.168319 | 2.837315e-03 | 2.520015e-02 | ||
| TNFA_SIGNALING_VIA_NFKB | rest | 2.968633 | 0.852557 | 4.833602e-03 | 3.452573e-02 | ||
| INFLAMMATORY_RESPONSE | rest | 2.863359 | 0.876734 | 6.539631e-03 | 4.087270e-02 | ||
| KRAS_SIGNALING_DN | rest | 2.723633 | 0.883038 | 9.217372e-03 | 4.762634e-02 | ||
| EPITHELIAL_MESENCHYMAL_TRANSITION | rest | 2.748511 | 1.207515 | 9.525267e-03 | 4.762634e-02 | ||
| COMPLEMENT | rest | 2.267542 | 0.770072 | 2.883656e-02 | 1.310753e-01 | ||
| MTORC1_SIGNALING | rest | 1.991202 | 0.559144 | 5.243595e-02 | 2.184831e-01 | ||
| MYC_TARGETS_V1 | rest | 1.757284 | 0.631625 | 8.645473e-02 | 3.043063e-01 | ||
| HYPOXIA | rest | 1.680201 | 0.463292 | 1.001070e-01 | 3.043063e-01 | ||
| NOTCH_SIGNALING | rest | 1.659145 | 0.530187 | 1.039100e-01 | 3.043063e-01 | ||
| CHOLESTEROL_HOMEOSTASIS | rest | 1.631818 | 0.439760 | 1.104984e-01 | 3.043063e-01 | ||
| ANGIOGENESIS | rest | 1.479370 | 0.677662 | 1.471858e-01 | 3.679644e-01 | ||
| UV_RESPONSE_DN | rest | 1.430943 | 0.449290 | 1.593721e-01 | 3.757224e-01 | ||
| KRAS_SIGNALING_UP | rest | 1.362844 | 0.452965 | 1.796567e-01 | 3.818708e-01 | ||
| TGF_BETA_SIGNALING | rest | 1.321252 | 0.436720 | 1.931622e-01 | 3.818708e-01 | ||
| IL2_STAT5_SIGNALING | rest | 1.308489 | 0.352776 | 1.973604e-01 | 3.818708e-01 | ||
| FATTY_ACID_METABOLISM | rest | 1.282750 | 0.326785 | 2.062102e-01 | 3.818708e-01 | ||
| REACTIVE_OXYGEN_SPECIES_PATHWAY | rest | 1.199875 | 0.331483 | 2.367610e-01 | 4.227874e-01 | ||
| PANCREAS_BETA_CELLS | rest | 1.059714 | 0.338602 | 2.949731e-01 | 5.085744e-01 | ||
| GLYCOLYSIS | rest | 0.915958 | 0.270276 | 3.645321e-01 | 5.879550e-01 | ||
| G2M_CHECKPOINT | rest | 0.768646 | 0.256997 | 4.463063e-01 | 6.554873e-01 | ||
| UV_RESPONSE_UP | rest | 0.545786 | 0.132503 | 5.879931e-01 | 7.541098e-01 | ||
| HEDGEHOG_SIGNALING | rest | 0.545731 | 0.170815 | 5.882056e-01 | 7.541098e-01 | ||
| XENOBIOTIC_METABOLISM | rest | 0.412141 | 0.116982 | 6.822219e-01 | 8.351453e-01 | ||
| E2F_TARGETS | rest | 0.408490 | 0.120569 | 6.848192e-01 | 8.351453e-01 | ||
| P53_PATHWAY | rest | 0.317519 | 0.074457 | 7.523022e-01 | 8.955978e-01 | ||
| ALLOGRAFT_REJECTION | rest | 0.215310 | 0.049521 | 8.305324e-01 | 9.331232e-01 | ||
| INTERFERON_ALPHA_RESPONSE | rest | 0.179327 | 0.052068 | 8.584850e-01 | 9.331359e-01 | ||
| IL6_JAK_STAT3_SIGNALING | rest | 0.032245 | 0.007541 | 9.744633e-01 | 9.943503e-01 | ||
| PI3K_AKT_MTOR_SIGNALING | rest | 0.005435 | 0.001441 | 9.956891e-01 | 9.956891e-01 | ||
| SnC Neuron | Ground Control | BILE_ACID_METABOLISM | rest | 2.184045 | 0.083742 | 2.908369e-02 | 3.462344e-02 |
| INTERFERON_GAMMA_RESPONSE | rest | 1.576291 | 0.069536 | 1.151292e-01 | 1.308287e-01 | ||
| MYC_TARGETS_V2 | rest | 1.208428 | 0.038149 | 2.270407e-01 | 2.522675e-01 | ||
| UV_RESPONSE_DN | rest | 0.710002 | 0.055284 | 4.777931e-01 | 5.082905e-01 | ||
| IL6_JAK_STAT3_SIGNALING | rest | 0.349561 | 0.012415 | 7.267074e-01 | 7.569868e-01 | ||
| Space Flight | OXIDATIVE_PHOSPHORYLATION | rest | 24.867264 | 3.164509 | 3.803166e-106 | 1.901583e-104 | |
| ADIPOGENESIS | rest | 19.737703 | 1.304684 | 6.612366e-75 | 1.653092e-73 | ||
| MYC_TARGETS_V1 | rest | 18.769672 | 1.695644 | 9.097074e-68 | 1.516179e-66 | ||
| MTORC1_SIGNALING | rest | 18.158076 | 1.227961 | 5.942483e-65 | 7.428103e-64 | ||
| UV_RESPONSE_UP | rest | 17.160545 | 1.131422 | 9.721386e-59 | 9.721386e-58 | ||
| FATTY_ACID_METABOLISM | rest | 15.305557 | 0.881125 | 2.710785e-48 | 2.258987e-47 | ||
| GLYCOLYSIS | rest | 15.228719 | 0.891421 | 5.308639e-48 | 3.791885e-47 | ||
| COMPLEMENT | rest | 14.015157 | 0.755307 | 1.541680e-41 | 9.635502e-41 | ||
| REACTIVE_OXYGEN_SPECIES_PATHWAY | rest | 12.728494 | 0.695763 | 7.303302e-35 | 4.057390e-34 | ||
| APOPTOSIS | rest | 12.638808 | 0.710934 | 1.770230e-34 | 8.851151e-34 | ||
| HYPOXIA | rest | 12.450656 | 0.718272 | 1.394905e-33 | 6.340479e-33 | ||
| ESTROGEN_RESPONSE_LATE | rest | 11.585392 | 0.618636 | 1.518012e-29 | 6.325051e-29 | ||
| MYOGENESIS | rest | 11.301750 | 0.694388 | 2.917710e-28 | 1.122196e-27 | ||
| UNFOLDED_PROTEIN_RESPONSE | rest | 9.987212 | 0.547767 | 1.220594e-22 | 4.359265e-22 | ||
| CHOLESTEROL_HOMEOSTASIS | rest | 9.915220 | 0.524105 | 2.416493e-22 | 8.054975e-22 | ||
| PI3K_AKT_MTOR_SIGNALING | rest | 9.878976 | 0.642550 | 3.331014e-22 | 1.040942e-21 | ||
| XENOBIOTIC_METABOLISM | rest | 9.574982 | 0.513808 | 6.020249e-21 | 1.770661e-20 | ||
| PROTEIN_SECRETION | rest | 9.154706 | 0.722858 | 2.161096e-19 | 6.003044e-19 | ||
| P53_PATHWAY | rest | 8.333394 | 0.437984 | 2.118340e-16 | 5.574580e-16 | ||
| KRAS_SIGNALING_UP | rest | 8.130886 | 0.444441 | 1.021171e-15 | 2.552926e-15 | ||
| PEROXISOME | rest | 6.991083 | 0.320431 | 4.450749e-12 | 1.059702e-11 | ||
| HEDGEHOG_SIGNALING | rest | 6.980738 | 0.481842 | 4.766437e-12 | 1.083281e-11 | ||
| TGF_BETA_SIGNALING | rest | 6.565013 | 0.464674 | 7.646655e-11 | 1.662316e-10 | ||
| ALLOGRAFT_REJECTION | rest | 6.210970 | 0.321179 | 7.184748e-10 | 1.496823e-09 | ||
| SPERMATOGENESIS | rest | 5.539049 | 0.360728 | 3.705268e-08 | 7.410536e-08 | ||
| APICAL_JUNCTION | rest | 5.319314 | 0.350762 | 1.233314e-07 | 2.371757e-07 | ||
| DNA_REPAIR | rest | 4.891647 | 0.238813 | 1.135231e-06 | 2.102280e-06 | ||
| COAGULATION | rest | 4.879114 | 0.289533 | 1.224318e-06 | 2.186282e-06 | ||
| APICAL_SURFACE | rest | 4.838355 | 0.287991 | 1.472595e-06 | 2.538957e-06 | ||
| EPITHELIAL_MESENCHYMAL_TRANSITION | rest | 4.697648 | 0.284127 | 2.925924e-06 | 4.876540e-06 | ||
| ANGIOGENESIS | rest | 4.368204 | 0.260928 | 1.356854e-05 | 2.188474e-05 | ||
| PANCREAS_BETA_CELLS | rest | 4.267481 | 0.285456 | 2.126539e-05 | 3.322718e-05 | ||
| G2M_CHECKPOINT | rest | 4.095392 | 0.268163 | 4.489328e-05 | 6.802012e-05 | ||
| TNFA_SIGNALING_VIA_NFKB | rest | 3.894946 | 0.247126 | 1.039087e-04 | 1.528069e-04 | ||
| ANDROGEN_RESPONSE | rest | 3.809841 | 0.205880 | 1.458522e-04 | 2.083603e-04 | ||
| MITOTIC_SPINDLE | rest | 3.534682 | 0.299265 | 4.234399e-04 | 5.881110e-04 | ||
| E2F_TARGETS | rest | 3.492490 | 0.178649 | 4.959027e-04 | 6.529159e-04 | ||
| HEME_METABOLISM | rest | 3.492007 | 0.193231 | 4.962161e-04 | 6.529159e-04 | ||
| IL2_STAT5_SIGNALING | rest | 3.161853 | 0.174202 | 1.606175e-03 | 2.059199e-03 | ||
| INFLAMMATORY_RESPONSE | rest | 2.564235 | 0.147879 | 1.045640e-02 | 1.307050e-02 | ||
| NOTCH_SIGNALING | rest | 2.070161 | 0.104380 | 3.864398e-02 | 4.712681e-02 | ||
| ESTROGEN_RESPONSE_EARLY | rest | 1.693146 | 0.092251 | 9.067799e-02 | 1.054395e-01 | ||
| KRAS_SIGNALING_DN | rest | 0.618357 | 0.042102 | 5.364528e-01 | 5.831009e-01 | ||
| WNT_BETA_CATENIN_SIGNALING | rest | 0.073239 | 0.003353 | 9.416277e-01 | 9.419449e-01 | ||
| INTERFERON_ALPHA_RESPONSE | rest | 0.072841 | 0.003374 | 9.419449e-01 | 9.419449e-01 | ||
| SnC Oligodendrocyte | Ground Control | ESTROGEN_RESPONSE_EARLY | rest | 2.090452 | 0.247138 | 3.775489e-02 | 1.348389e-01 |
| TGF_BETA_SIGNALING | rest | 1.542515 | 0.261775 | 1.244371e-01 | 2.592440e-01 | ||
| UV_RESPONSE_DN | rest | 1.509263 | 0.233542 | 1.326806e-01 | 2.653612e-01 | ||
| TNFA_SIGNALING_VIA_NFKB | rest | 1.381708 | 0.139286 | 1.684759e-01 | 3.119924e-01 | ||
| MITOTIC_SPINDLE | rest | 1.306547 | 0.218913 | 1.927452e-01 | 3.295562e-01 | ||
| IL6_JAK_STAT3_SIGNALING | rest | 1.291977 | 0.150724 | 1.977337e-01 | 3.295562e-01 | ||
| HEDGEHOG_SIGNALING | rest | 0.781868 | 0.108513 | 4.351406e-01 | 5.578725e-01 | ||
| BILE_ACID_METABOLISM | rest | 0.598056 | 0.070221 | 5.504251e-01 | 6.712501e-01 | ||
| NOTCH_SIGNALING | rest | 0.385267 | 0.049330 | 7.004224e-01 | 7.782471e-01 | ||
| ALLOGRAFT_REJECTION | rest | 0.321177 | 0.045313 | 7.483883e-01 | 7.961578e-01 | ||
| INTERFERON_GAMMA_RESPONSE | rest | 0.261029 | 0.032342 | 7.943190e-01 | 8.252044e-01 | ||
| KRAS_SIGNALING_UP | rest | 0.242397 | 0.034709 | 8.087003e-01 | 8.252044e-01 | ||
| Space Flight | OXIDATIVE_PHOSPHORYLATION | rest | 10.621847 | 1.614291 | 1.290951e-21 | 6.454754e-20 | |
| ADIPOGENESIS | rest | 6.240164 | 0.830911 | 1.901992e-09 | 4.754981e-08 | ||
| MTORC1_SIGNALING | rest | 5.839303 | 0.689714 | 1.627953e-08 | 2.713255e-07 | ||
| MYC_TARGETS_V1 | rest | 5.781524 | 0.733480 | 2.202246e-08 | 2.752807e-07 | ||
| CHOLESTEROL_HOMEOSTASIS | rest | 4.715715 | 0.631090 | 3.976415e-06 | 3.976415e-05 | ||
| XENOBIOTIC_METABOLISM | rest | 4.594047 | 0.535270 | 6.877921e-06 | 5.731601e-05 | ||
| FATTY_ACID_METABOLISM | rest | 4.108890 | 0.473116 | 5.434216e-05 | 3.881583e-04 | ||
| COAGULATION | rest | 4.018363 | 0.507803 | 7.727186e-05 | 4.829491e-04 | ||
| REACTIVE_OXYGEN_SPECIES_PATHWAY | rest | 3.815481 | 0.416689 | 1.715913e-04 | 9.532849e-04 | ||
| PEROXISOME | rest | 3.487262 | 0.369153 | 5.796031e-04 | 2.660737e-03 | ||
| MYOGENESIS | rest | 3.482957 | 0.418157 | 5.853622e-04 | 2.660737e-03 | ||
| ESTROGEN_RESPONSE_LATE | rest | 3.013350 | 0.337196 | 2.845120e-03 | 1.185467e-02 | ||
| UV_RESPONSE_UP | rest | 2.512959 | 0.297640 | 1.261915e-02 | 4.853519e-02 | ||
| APOPTOSIS | rest | 2.181628 | 0.259184 | 3.005681e-02 | 1.001894e-01 | ||
| COMPLEMENT | rest | 2.124511 | 0.270481 | 3.460229e-02 | 1.081322e-01 | ||
| KRAS_SIGNALING_DN | rest | 1.902713 | 0.237761 | 5.823757e-02 | 1.712870e-01 | ||
| INFLAMMATORY_RESPONSE | rest | 1.824169 | 0.212521 | 6.930214e-02 | 1.925059e-01 | ||
| PI3K_AKT_MTOR_SIGNALING | rest | 1.793655 | 0.204911 | 7.409651e-02 | 1.929206e-01 | ||
| DNA_REPAIR | rest | 1.742478 | 0.160966 | 8.263904e-02 | 1.929206e-01 | ||
| GLYCOLYSIS | rest | 1.740437 | 0.196336 | 8.299339e-02 | 1.929206e-01 | ||
| EPITHELIAL_MESENCHYMAL_TRANSITION | rest | 1.729782 | 0.212366 | 8.488509e-02 | 1.929206e-01 | ||
| UNFOLDED_PROTEIN_RESPONSE | rest | 1.704517 | 0.170177 | 8.954355e-02 | 1.946599e-01 | ||
| PANCREAS_BETA_CELLS | rest | 1.512385 | 0.159588 | 1.317006e-01 | 2.532705e-01 | ||
| P53_PATHWAY | rest | 1.422636 | 0.150105 | 1.560845e-01 | 2.743878e-01 | ||
| INTERFERON_ALPHA_RESPONSE | rest | 1.291190 | 0.139284 | 1.978169e-01 | 3.190595e-01 | ||
| PROTEIN_SECRETION | rest | 1.230285 | 0.166442 | 2.197292e-01 | 3.433269e-01 | ||
| E2F_TARGETS | rest | 1.192953 | 0.111780 | 2.340015e-01 | 3.545477e-01 | ||
| IL2_STAT5_SIGNALING | rest | 1.071580 | 0.139397 | 2.849653e-01 | 4.190667e-01 | ||
| ANDROGEN_RESPONSE | rest | 1.029095 | 0.143171 | 3.044195e-01 | 4.348851e-01 | ||
| HYPOXIA | rest | 0.947164 | 0.104918 | 3.444606e-01 | 4.725928e-01 | ||
| G2M_CHECKPOINT | rest | 0.936873 | 0.117459 | 3.497187e-01 | 4.725928e-01 | ||
| APICAL_JUNCTION | rest | 0.882673 | 0.116597 | 3.782491e-01 | 4.976962e-01 | ||
| HEME_METABOLISM | rest | 0.779315 | 0.104651 | 4.365207e-01 | 5.456509e-01 | ||
| MYC_TARGETS_V2 | rest | 0.600183 | 0.044014 | 5.489215e-01 | 6.534780e-01 | ||
| ANGIOGENESIS | rest | 0.489974 | 0.062189 | 6.245766e-01 | 7.232267e-01 | ||
| WNT_BETA_CATENIN_SIGNALING | rest | 0.473253 | 0.053997 | 6.364395e-01 | 7.232267e-01 | ||
| APICAL_SURFACE | rest | 0.392503 | 0.056158 | 6.950197e-01 | 7.554562e-01 | ||
| SPERMATOGENESIS | rest | 0.232778 | 0.030223 | 8.161211e-01 | 8.161211e-01 | ||
| SnC Endothelial | Ground Control | MITOTIC_SPINDLE | rest | 3.258011 | 1.478947 | 3.805116e-03 | 8.083137e-02 |
| TGF_BETA_SIGNALING | rest | 2.941355 | 1.384740 | 7.676547e-03 | 8.083137e-02 | ||
| APOPTOSIS | rest | 2.975580 | 1.335171 | 8.857444e-03 | 8.083137e-02 | ||
| IL6_JAK_STAT3_SIGNALING | rest | 2.816275 | 0.752832 | 1.072240e-02 | 8.083137e-02 | ||
| PROTEIN_SECRETION | rest | 2.564215 | 1.007616 | 1.887272e-02 | 1.048484e-01 | ||
| NOTCH_SIGNALING | rest | 2.488042 | 0.896804 | 2.216619e-02 | 1.108309e-01 | ||
| APICAL_JUNCTION | rest | 2.093046 | 1.157169 | 5.073864e-02 | 1.951486e-01 | ||
| REACTIVE_OXYGEN_SPECIES_PATHWAY | rest | 1.935321 | 0.634651 | 6.683747e-02 | 2.387053e-01 | ||
| ANDROGEN_RESPONSE | rest | 1.807020 | 0.714925 | 8.514720e-02 | 2.838240e-01 | ||
| HEDGEHOG_SIGNALING | rest | 1.613041 | 0.647814 | 1.210153e-01 | 3.559274e-01 | ||
| E2F_TARGETS | rest | 1.575661 | 0.524390 | 1.342795e-01 | 3.729987e-01 | ||
| MTORC1_SIGNALING | rest | 1.402324 | 0.762088 | 1.751138e-01 | 4.608259e-01 | ||
| IL2_STAT5_SIGNALING | rest | 1.080739 | 0.400099 | 2.956850e-01 | 6.720114e-01 | ||
| G2M_CHECKPOINT | rest | 1.039856 | 0.522363 | 3.107505e-01 | 6.755446e-01 | ||
| MYC_TARGETS_V1 | rest | 0.824629 | 0.596456 | 4.185894e-01 | 7.474810e-01 | ||
| MYOGENESIS | rest | 0.726675 | 0.557906 | 4.755678e-01 | 7.809173e-01 | ||
| UV_RESPONSE_DN | rest | 0.680010 | 0.291038 | 5.080616e-01 | 7.809173e-01 | ||
| EPITHELIAL_MESENCHYMAL_TRANSITION | rest | 0.675980 | 0.277083 | 5.113815e-01 | 7.809173e-01 | ||
| TNFA_SIGNALING_VIA_NFKB | rest | 0.480988 | 0.249353 | 6.353168e-01 | 8.585362e-01 | ||
| UNFOLDED_PROTEIN_RESPONSE | rest | 0.397496 | 0.188742 | 6.948673e-01 | 8.890824e-01 | ||
| BILE_ACID_METABOLISM | rest | 0.328097 | 0.152519 | 7.461721e-01 | 8.899172e-01 | ||
| PEROXISOME | rest | 0.325984 | 0.159601 | 7.475304e-01 | 8.899172e-01 | ||
| APICAL_SURFACE | rest | 0.224855 | 0.087958 | 8.248180e-01 | 9.590907e-01 | ||
| CHOLESTEROL_HOMEOSTASIS | rest | 0.091858 | 0.063601 | 9.277623e-01 | 9.667337e-01 | ||
| ESTROGEN_RESPONSE_LATE | rest | 0.088513 | 0.054675 | 9.303993e-01 | 9.667337e-01 | ||
| MYC_TARGETS_V2 | rest | 0.055319 | 0.013706 | 9.567997e-01 | 9.667337e-01 | ||
| Space Flight | P53_PATHWAY | rest | 1.656970 | 1.154408 | 2.227517e-01 | 9.843963e-01 | |
| FATTY_ACID_METABOLISM | rest | 1.440087 | 1.248901 | 2.285704e-01 | 9.843963e-01 | ||
| KRAS_SIGNALING_DN | rest | 1.382228 | 1.793627 | 2.391280e-01 | 9.843963e-01 | ||
| UV_RESPONSE_UP | rest | 1.349375 | 1.457897 | 2.501123e-01 | 9.843963e-01 | ||
| KRAS_SIGNALING_UP | rest | 1.210298 | 0.858342 | 3.084082e-01 | 9.843963e-01 | ||
| ADIPOGENESIS | rest | 1.143115 | 1.153930 | 3.382869e-01 | 9.843963e-01 | ||
| PANCREAS_BETA_CELLS | rest | 0.874927 | 0.755823 | 4.444977e-01 | 9.843963e-01 | ||
| INFLAMMATORY_RESPONSE | rest | 0.686299 | 0.402708 | 5.578380e-01 | 9.843963e-01 | ||
| WNT_BETA_CATENIN_SIGNALING | rest | 0.542168 | 0.416588 | 6.185467e-01 | 9.843963e-01 | ||
| GLYCOLYSIS | rest | 0.504993 | 0.361742 | 6.537687e-01 | 9.843963e-01 | ||
| INTERFERON_GAMMA_RESPONSE | rest | 0.473884 | 0.620932 | 6.603920e-01 | 9.843963e-01 | ||
| HEME_METABOLISM | rest | 0.475097 | 0.373352 | 6.753055e-01 | 9.843963e-01 | ||
| ESTROGEN_RESPONSE_EARLY | rest | 0.463498 | 0.289785 | 6.786472e-01 | 9.843963e-01 | ||
| COMPLEMENT | rest | 0.337482 | 0.354969 | 7.595126e-01 | 9.843963e-01 | ||
| ANGIOGENESIS | rest | 0.321828 | 0.330454 | 7.739396e-01 | 9.843963e-01 | ||
| OXIDATIVE_PHOSPHORYLATION | rest | 0.323035 | 0.355209 | 7.746842e-01 | 9.843963e-01 | ||
| XENOBIOTIC_METABOLISM | rest | 0.268079 | 0.188343 | 8.110869e-01 | 9.843963e-01 | ||
| DNA_REPAIR | rest | 0.260233 | 0.177456 | 8.111853e-01 | 9.843963e-01 | ||
| SPERMATOGENESIS | rest | 0.204693 | 0.210566 | 8.550464e-01 | 9.843963e-01 | ||
| HYPOXIA | rest | 0.188765 | 0.199785 | 8.638931e-01 | 9.843963e-01 | ||
| COAGULATION | rest | 0.075431 | 0.061910 | 9.446363e-01 | 9.843963e-01 | ||
| INTERFERON_ALPHA_RESPONSE | rest | 0.074280 | 0.080148 | 9.462098e-01 | 9.843963e-01 | ||
| PI3K_AKT_MTOR_SIGNALING | rest | 0.068150 | 0.048184 | 9.515520e-01 | 9.843963e-01 | ||
| ALLOGRAFT_REJECTION | rest | 0.021158 | 0.026526 | 9.843963e-01 | 9.843963e-01 | ||
| SnC Microglial | Ground Control | EPITHELIAL_MESENCHYMAL_TRANSITION | rest | 1.440658 | 0.404078 | 1.553477e-01 | 5.591584e-01 |
| PEROXISOME | rest | 1.373162 | 0.358059 | 1.752125e-01 | 5.591584e-01 | ||
| IL6_JAK_STAT3_SIGNALING | rest | 1.372558 | 0.350405 | 1.757632e-01 | 5.591584e-01 | ||
| BILE_ACID_METABOLISM | rest | 1.260926 | 0.299801 | 2.126703e-01 | 5.907507e-01 | ||
| MYOGENESIS | rest | 0.771268 | 0.194101 | 4.437985e-01 | 7.651699e-01 | ||
| UV_RESPONSE_UP | rest | 0.673652 | 0.165443 | 5.034820e-01 | 8.120677e-01 | ||
| G2M_CHECKPOINT | rest | 0.517848 | 0.139623 | 6.066064e-01 | 8.398744e-01 | ||
| NOTCH_SIGNALING | rest | 0.348341 | 0.115534 | 7.290534e-01 | 8.413074e-01 | ||
| PANCREAS_BETA_CELLS | rest | 0.336158 | 0.085310 | 7.380893e-01 | 8.413074e-01 | ||
| ESTROGEN_RESPONSE_LATE | rest | 0.230938 | 0.051052 | 8.183012e-01 | 9.075081e-01 | ||
| INFLAMMATORY_RESPONSE | rest | 0.204267 | 0.051541 | 8.389214e-01 | 9.075081e-01 | ||
| IL2_STAT5_SIGNALING | rest | 0.141234 | 0.033671 | 8.881926e-01 | 9.075081e-01 | ||
| ESTROGEN_RESPONSE_EARLY | rest | 0.123668 | 0.033802 | 9.020303e-01 | 9.075081e-01 | ||
| Space Flight | OXIDATIVE_PHOSPHORYLATION | rest | 3.811117 | 0.848163 | 3.066829e-04 | 1.533415e-02 | |
| SPERMATOGENESIS | rest | 2.718507 | 0.431131 | 8.465016e-03 | 1.726303e-01 | ||
| ALLOGRAFT_REJECTION | rest | 2.449465 | 0.711605 | 1.699491e-02 | 1.726303e-01 | ||
| ADIPOGENESIS | rest | 2.434296 | 0.504914 | 1.764277e-02 | 1.726303e-01 | ||
| FATTY_ACID_METABOLISM | rest | 2.384960 | 0.439813 | 1.997116e-02 | 1.726303e-01 | ||
| REACTIVE_OXYGEN_SPECIES_PATHWAY | rest | 2.374217 | 0.515640 | 2.071563e-02 | 1.726303e-01 | ||
| MYC_TARGETS_V1 | rest | 2.253054 | 0.510957 | 2.768355e-02 | 1.751217e-01 | ||
| APOPTOSIS | rest | 2.230415 | 0.529620 | 2.917642e-02 | 1.751217e-01 | ||
| HEME_METABOLISM | rest | 2.174849 | 0.515986 | 3.341929e-02 | 1.751217e-01 | ||
| KRAS_SIGNALING_UP | rest | 2.152448 | 0.506274 | 3.502433e-02 | 1.751217e-01 | ||
| GLYCOLYSIS | rest | 1.819892 | 0.377820 | 7.332269e-02 | 3.332849e-01 | ||
| COMPLEMENT | rest | 1.644387 | 0.436168 | 1.048592e-01 | 4.369132e-01 | ||
| INTERFERON_GAMMA_RESPONSE | rest | 1.474014 | 0.424819 | 1.452898e-01 | 4.540305e-01 | ||
| WNT_BETA_CATENIN_SIGNALING | rest | 1.376790 | 0.311849 | 1.734985e-01 | 4.913533e-01 | ||
| MYC_TARGETS_V2 | rest | 1.231202 | 0.196730 | 2.226711e-01 | 5.859765e-01 | ||
| MTORC1_SIGNALING | rest | 1.183149 | 0.238531 | 2.410463e-01 | 6.026158e-01 | ||
| APICAL_SURFACE | rest | 1.125263 | 0.263601 | 2.645534e-01 | 6.129812e-01 | ||
| APICAL_JUNCTION | rest | 1.037779 | 0.245304 | 3.032191e-01 | 6.129812e-01 | ||
| TNFA_SIGNALING_VIA_NFKB | rest | 1.034940 | 0.257406 | 3.045002e-01 | 6.129812e-01 | ||
| TGF_BETA_SIGNALING | rest | 1.023306 | 0.328814 | 3.099090e-01 | 6.129812e-01 | ||
| ANDROGEN_RESPONSE | rest | 1.010718 | 0.243995 | 3.158426e-01 | 6.129812e-01 | ||
| PI3K_AKT_MTOR_SIGNALING | rest | 1.004683 | 0.254420 | 3.187502e-01 | 6.129812e-01 | ||
| P53_PATHWAY | rest | 0.933371 | 0.203040 | 3.540307e-01 | 6.556124e-01 | ||
| HYPOXIA | rest | 0.841298 | 0.201779 | 4.032467e-01 | 7.011621e-01 | ||
| CHOLESTEROL_HOMEOSTASIS | rest | 0.778259 | 0.181943 | 4.392603e-01 | 7.321005e-01 | ||
| ANGIOGENESIS | rest | 0.596020 | 0.133813 | 5.532715e-01 | 8.033104e-01 | ||
| KRAS_SIGNALING_DN | rest | 0.582424 | 0.134041 | 5.624000e-01 | 8.033104e-01 | ||
| COAGULATION | rest | 0.566692 | 0.197444 | 5.729323e-01 | 8.033104e-01 | ||
| INTERFERON_ALPHA_RESPONSE | rest | 0.538127 | 0.138115 | 5.923003e-01 | 8.033104e-01 | ||
| XENOBIOTIC_METABOLISM | rest | 0.532792 | 0.117440 | 5.959806e-01 | 8.033104e-01 | ||
| UV_RESPONSE_DN | rest | 0.511825 | 0.162355 | 6.105159e-01 | 8.033104e-01 | ||
| PROTEIN_SECRETION | rest | 0.416829 | 0.104737 | 6.781942e-01 | 8.176644e-01 | ||
| HEDGEHOG_SIGNALING | rest | 0.381716 | 0.091101 | 7.038987e-01 | 8.176644e-01 | ||
| E2F_TARGETS | rest | 0.378163 | 0.077710 | 7.065556e-01 | 8.176644e-01 | ||
| UNFOLDED_PROTEIN_RESPONSE | rest | 0.360619 | 0.069374 | 7.195447e-01 | 8.176644e-01 | ||
| MITOTIC_SPINDLE | rest | 0.192950 | 0.048890 | 8.476142e-01 | 8.998196e-01 | ||
| DNA_REPAIR | rest | 0.126399 | 0.026469 | 8.998196e-01 | 8.998196e-01 | ||
| SnC Astrocyte | Ground Control | INTERFERON_ALPHA_RESPONSE | rest | 1.456071 | 0.189438 | 1.480762e-01 | 3.219047e-01 |
| APICAL_JUNCTION | rest | 1.145709 | 0.197663 | 2.542790e-01 | 4.101274e-01 | ||
| ALLOGRAFT_REJECTION | rest | 0.901015 | 0.167060 | 3.694925e-01 | 5.598371e-01 | ||
| UV_RESPONSE_DN | rest | 0.781900 | 0.165101 | 4.358761e-01 | 6.226802e-01 | ||
| ANGIOGENESIS | rest | 0.743924 | 0.161066 | 4.585001e-01 | 6.368057e-01 | ||
| HEME_METABOLISM | rest | 0.664483 | 0.129212 | 5.077334e-01 | 6.488810e-01 | ||
| INTERFERON_GAMMA_RESPONSE | rest | 0.642541 | 0.097926 | 5.217902e-01 | 6.488810e-01 | ||
| APICAL_SURFACE | rest | 0.530951 | 0.084415 | 5.964695e-01 | 7.100827e-01 | ||
| IL2_STAT5_SIGNALING | rest | 0.448764 | 0.086566 | 6.544502e-01 | 7.609886e-01 | ||
| WNT_BETA_CATENIN_SIGNALING | rest | 0.293189 | 0.039791 | 7.699485e-01 | 8.384899e-01 | ||
| PANCREAS_BETA_CELLS | rest | 0.291212 | 0.051798 | 7.714107e-01 | 8.384899e-01 | ||
| TGF_BETA_SIGNALING | rest | 0.227882 | 0.049780 | 8.201393e-01 | 8.543118e-01 | ||
| IL6_JAK_STAT3_SIGNALING | rest | 0.092210 | 0.013979 | 9.266954e-01 | 9.456076e-01 | ||
| Space Flight | OXIDATIVE_PHOSPHORYLATION | rest | 4.135787 | 1.269167 | 6.578064e-05 | 3.289032e-03 | |
| COAGULATION | rest | 3.827156 | 0.841291 | 2.084334e-04 | 5.210834e-03 | ||
| ADIPOGENESIS | rest | 3.295846 | 0.813558 | 1.305001e-03 | 2.175002e-02 | ||
| CHOLESTEROL_HOMEOSTASIS | rest | 3.073215 | 0.743432 | 2.627684e-03 | 2.972019e-02 | ||
| REACTIVE_OXYGEN_SPECIES_PATHWAY | rest | 3.031995 | 0.521165 | 2.972019e-03 | 2.972019e-02 | ||
| G2M_CHECKPOINT | rest | 2.845482 | 0.507279 | 5.204833e-03 | 4.317580e-02 | ||
| MYC_TARGETS_V1 | rest | 2.797510 | 0.528136 | 6.044612e-03 | 4.317580e-02 | ||
| HYPOXIA | rest | 2.681428 | 0.606257 | 8.418665e-03 | 4.933496e-02 | ||
| NOTCH_SIGNALING | rest | 2.659382 | 0.486780 | 8.880294e-03 | 4.933496e-02 | ||
| MTORC1_SIGNALING | rest | 2.569426 | 0.675254 | 1.141048e-02 | 5.224883e-02 | ||
| GLYCOLYSIS | rest | 2.568020 | 0.495314 | 1.149474e-02 | 5.224883e-02 | ||
| SPERMATOGENESIS | rest | 2.328058 | 0.386120 | 2.162334e-02 | 9.009725e-02 | ||
| XENOBIOTIC_METABOLISM | rest | 2.187923 | 0.464307 | 3.060893e-02 | 1.177267e-01 | ||
| FATTY_ACID_METABOLISM | rest | 1.927243 | 0.408874 | 5.629439e-02 | 2.010514e-01 | ||
| PROTEIN_SECRETION | rest | 1.891015 | 0.356060 | 6.102348e-02 | 2.034116e-01 | ||
| KRAS_SIGNALING_UP | rest | 1.680811 | 0.321696 | 9.538339e-02 | 2.980731e-01 | ||
| P53_PATHWAY | rest | 1.622111 | 0.276149 | 1.073792e-01 | 3.002548e-01 | ||
| COMPLEMENT | rest | 1.590685 | 0.356450 | 1.142690e-01 | 3.002548e-01 | ||
| INFLAMMATORY_RESPONSE | rest | 1.581224 | 0.352387 | 1.164165e-01 | 3.002548e-01 | ||
| ANDROGEN_RESPONSE | rest | 1.563807 | 0.270782 | 1.205143e-01 | 3.002548e-01 | ||
| EPITHELIAL_MESENCHYMAL_TRANSITION | rest | 1.537647 | 0.299789 | 1.268488e-01 | 3.002548e-01 | ||
| PEROXISOME | rest | 1.498559 | 0.274023 | 1.366439e-01 | 3.002548e-01 | ||
| E2F_TARGETS | rest | 1.434435 | 0.170458 | 1.540362e-01 | 3.209088e-01 | ||
| DNA_REPAIR | rest | 1.357480 | 0.181921 | 1.771480e-01 | 3.464186e-01 | ||
| TNFA_SIGNALING_VIA_NFKB | rest | 1.328115 | 0.259823 | 1.866453e-01 | 3.464186e-01 | ||
| ESTROGEN_RESPONSE_LATE | rest | 1.322312 | 0.275938 | 1.885408e-01 | 3.464186e-01 | ||
| APOPTOSIS | rest | 1.306045 | 0.231854 | 1.939944e-01 | 3.464186e-01 | ||
| MYOGENESIS | rest | 1.279678 | 0.255203 | 2.031209e-01 | 3.502085e-01 | ||
| UV_RESPONSE_UP | rest | 1.204659 | 0.239568 | 2.306683e-01 | 3.844471e-01 | ||
| KRAS_SIGNALING_DN | rest | 1.042138 | 0.198434 | 2.994240e-01 | 4.678500e-01 | ||
| MYC_TARGETS_V2 | rest | 0.881438 | 0.100794 | 3.798556e-01 | 5.586112e-01 | ||
| PI3K_AKT_MTOR_SIGNALING | rest | 0.730555 | 0.134849 | 4.664547e-01 | 6.303442e-01 | ||
| ESTROGEN_RESPONSE_EARLY | rest | 0.657379 | 0.146514 | 5.121758e-01 | 6.363304e-01 | ||
| HEDGEHOG_SIGNALING | rest | 0.642444 | 0.137294 | 5.217909e-01 | 6.363304e-01 | ||
| BILE_ACID_METABOLISM | rest | 0.376496 | 0.069578 | 7.072089e-01 | 8.036465e-01 | ||
| UNFOLDED_PROTEIN_RESPONSE | rest | 0.234439 | 0.037217 | 8.150927e-01 | 8.496711e-01 | ||
| MITOTIC_SPINDLE | rest | 0.003854 | 0.000840 | 9.969314e-01 | 9.969314e-01 |
Individual Cell Scores¶
In [ ]:
res_aucell = scflow.ax.run_decoupler_aucell(
self.rna, col_covariates=["Senescent_Cell_Label_by_Type", col_condition],
resource="progeny", species=species, inplace=True)
_ = self.plot(genes=list(self.rna.obsm["score_aucell"].columns),
subset=self.rna.obs["Senescent_Cell"],
col_celltype=col_condition, kind="violin",
by_group="Senescent_Cell_Label_by_Type", figsize=(15, 15))
DEGs (find_markers) by Group¶
In [ ]:
out_pathway_snc_degs = scflow.ax.run_enrichr(
df_snc_markers_top, col_grouping=["Comparison", col_celltype],
gene_sets=gene_sets, fx_replace_string=fx_replace_string, title="Overall")
out_pathway_snc_degs_gc = scflow.ax.run_enrichr(
df_snc_markers_top_gc, col_grouping=["Comparison", col_celltype],
gene_sets=gene_sets, fx_replace_string=fx_replace_string,
title=keys[col_condition]["key_control"])
out_pathway_snc_degs_sf = scflow.ax.run_enrichr(
df_snc_markers_top_sf, col_grouping=["Comparison", col_celltype],
gene_sets=gene_sets, fx_replace_string=fx_replace_string,
title=keys[col_condition]["key_treatment"])
# out_pathway_snc_degs[1].drop("All", level=0)
Edger Contrasts (Pseudo-Bulk)¶
In [ ]:
# GSEA
print("\n\nGSEA Enrichment Analysis\n\n")
out_pathway_snc_degs = scflow.ax.run_enrichr(
out_edgr_contrasts_snc_all_top, col_grouping=col_celltype,
gene_sets=gene_sets, fx_replace_string=fx_replace_string)
# ToppGene
print("\n\nToppGene Enrichment Analysis\n\n")
mks_ctrsts_snc = dict(out_edgr_contrasts_snc_all_top.reset_index(
1).variable.groupby(col_celltype).apply(lambda x: list(x))) # SnC DEGs
results_toppgene_degs = scflow.pp.annotate_by_toppgene(
mks_ctrsts_snc, species=species, min_genes=1,
categories=["Pathway", "PubMed"])
results_toppgene_degs = results_toppgene_degs.join(pd.Series(
mks_ctrsts_snc).to_frame("DEGs").rename_axis("Gene Set"))
results_toppgene_degs = results_toppgene_degs.groupby("Gene Set").apply(
lambda x: x.sort_values("PValue", ascending=True), include_groups=False)
pthwys = dict(results_toppgene_degs.groupby("Gene Set").apply(
lambda x: [i.capitalize() for i in x.Name.unique()]))
print("\n\n".join([f"{k}: {pthwys[k]}" for k in pthwys]))
results_toppgene_degs[["Name", "Symbols", "GenesInTerm", "GenesInQuery",
"GenesInTermInQuery", "PValue"]].reset_index(
2, drop=True)
Plot¶
In [ ]:
# Top 10 Senescence Genes or All?
ggg = top_genes
# ggg = genes
# Mask for High Senescence Subset & Helper Variable for Figure Dimensions
subset = self.rna.obs["Senescent_Cell"].isin([1, True])
num_cts = len(self.rna[subset].obs[col_celltype].unique())
# Plot SnC Markers Overlap with Marker Sets
sc.pl.violin(self.rna[subset], set(genes).intersection(
marker_gene_sets.explode().unique()),
groupby=col_batch, rotation=90) # plot overlap w/ SnC markers
# Plot Most Highly-Expressed Genes in Each Senescent Cell Type
_ = self.plot(kind=["matrix", "heat"], genes=top_genes,
col_celltype=col_celltype, layer="log1p",
subset=subset, dendrogram=True, swap_axes=False, heat=dict(
dendrogram=False), standard_scale="var",
figsize=(len(top_genes) / 1.5, num_cts * 1.5))
# Grouped by Top Cell Types
_ = self.plot(kind=["matrix", "heat"], genes=top_genes_dict,
standard_scale="obs", col_celltype=col_celltype,
layer="log1p", swap_axes=False, figsize=(len(ggg), num_cts),
subset=subset, dendrogram=True)
Write Object¶
In [ ]:
# Send Email with Output When Done
if email is not None and html_out is not None:
os.system(f"jupyter nbconvert --to html {cur_file}")
os.system(f"echo 'yay' | mutt -s 'JOB DONE' -a {html_out} -- {email}")
if overwrite is True or os.path.exists() is False:
self.rna.write_h5ad(file_path_new)
else:
print(f"\n\n\n{'=' * 120}\nOVERWRITE IS FALSE..."
f"NOT WRITING OBJECT!!!\n{'=' * 120}\n\n\n")
SCRATCH¶
In [ ]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
df_degs = df_snc_markers.drop("All").reset_index(0, drop=True)
factor_name = col_celltype
# factor_name = col_condition
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
gene_sets = dict(zip(["up", "down"], [[
f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
for i in ["up", "down"]]))
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
In [ ]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
# factor_name = col_celltype
# factor_name = col_condition
factor_name = col_batch
df_degs = res_rank_genes_top.loc[:, "Overall", :]
df_degs = df_degs.assign(log_fc=df_degs.logfoldchanges.abs())
df_degs = df_degs.assign(abs_log_fc=df_degs.logfoldchanges_abs)
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs",
fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
In [ ]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
factor_name = col_celltype
# factor_name = col_condition
iii = 2
df_degs = res_rank_genes_top.loc[self.rna.obs[col_batch].unique()[iii]]
df_degs = df_degs.assign(log_fc=df_degs.logfoldchanges.abs())
df_degs = df_degs.assign(abs_log_fc=df_degs.logfoldchanges_abs)
print(self.rna.obs[col_batch].unique()[iii])
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
In [ ]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
factor_name = col_celltype
# factor_name = col_condition
iii = 1
df_degs = res_rank_genes_top.loc[self.rna.obs[col_batch].unique()[iii]]
df_degs = df_degs.assign(log_fc=df_degs.logfoldchanges.abs())
df_degs = df_degs.assign(abs_log_fc=df_degs.logfoldchanges_abs)
print(self.rna.obs[col_batch].unique()[iii])
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]